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How To Fine-Tune Segment Anything

Computer vision is having its ChatGPT moment with the release of the Segment Anything Model (SAM) by Meta last week. Trained over 11 billion segmentation masks, SAM is a foundation model for predictive AI use cases rather than generative AI. While it has shown an incredible amount of flexibility in its ability to segment over wide-ranging image modalities and problem spaces, it was released without “fine-tuning” functionality. This tutorial will outline some of the key steps to fine-tune SAM using the mask decoder, particularly describing which functions from SAM to use to pre/post-process the data so that it's in good shape for fine-tuning. {{Training_data_CTA::Supercharge your annotations by fine-tuning SAM for your use case}} What is the Segment Anything Model (SAM)? The Segment Anything Model (SAM) is a segmentation model developed by Meta AI. It is considered the first foundational model for Computer Vision. SAM was trained on a huge corpus of data containing millions of images and billions of masks, making it extremely powerful. As its name suggests, SAM is able to produce accurate segmentation masks for a wide variety of images. SAM’s design allows it to take human prompts into account, making it particularly powerful for Human In The Loop annotation. These prompts can be multi-modal: they can be points on the area to be segmented, a bounding box around the object to be segmented, or a text prompt about what should be segmented. The model is structured into 3 components: an image encoder, a prompt encoder, and a mask decoder. Source The image encoder generates an embedding for the image being segmented, whilst the prompt encoder generates an embedding for the prompts. The image encoder is a particularly large component of the model. This is in contrast to the lightweight mask decoder, which predicts segmentation masks based on the embeddings. Meta AI has made the weights and biases of the model trained on the Segment Anything 1 Billion Mask (SA-1B) dataset available as a model checkpoint. {{light_callout_start}} Learn more about how Segment Anything works in our explainer blog post Segment Anything Model (SAM) Explained. {{light_callout_end}} What is Model Fine-Tuning? Publicly available state-of-the-art models have a custom architecture and are typically supplied with pre-trained model weights. If these architectures were supplied without weights then the models would need to be trained from scratch by the users, who would need to use massive datasets to obtain state-of-the-art performance. Model fine-tuning is the process of taking a pre-trained model (architecture+weights) and showing it data for a particular use case. This will typically be data that the model hasn’t seen before, or that is underrepresented in its original training dataset. The difference between fine-tuning the model and starting from scratch is the starting value of the weights and biases. If we were training from scratch, these would be randomly initialized according to some strategy. In such a starting configuration, the model would ‘know nothing’ of the task at hand and perform poorly. By using pre-existing weights and biases as a starting point we can ‘fine tune’ the weights and biases so that our model works better on our custom dataset. For example, the information learned to recognize cats (edge detection, counting paws) will be useful for recognizing dogs. Why Would I Fine-Tune a Model? The purpose of fine-tuning a model is to obtain higher performance on data that the pre-trained model has not seen before. For example, an image segmentation model trained on a broad corpus of data gathered from phone cameras will have mostly seen images from a horizontal perspective. If we tried to use this model for satellite imagery taken from a vertical perspective, it may not perform as well. If we were trying to segment rooftops, the model may not yield the best results. The pre-training is useful because the model will have learned how to segment objects in general, so we want to take advantage of this starting point to build a model that can accurately segment rooftops. Furthermore, it is likely that our custom dataset would not have millions of examples, so we want to fine-tune instead of training the model from scratch. Fine tuning is desirable so that we can obtain better performance on our specific use case, without having to incur the computational cost of training a model from scratch. How to Fine-Tune Segment Anything Model [With Code] Background & Architecture We gave an overview of the SAM architecture in the introduction section. The image encoder has a complex architecture with many parameters. In order to fine-tune the model, it makes sense for us to focus on the mask decoder which is lightweight and therefore easier, faster, and more memory efficient to fine-tune. In order to fine-tune SAM, we need to extract the underlying pieces of its architecture (image and prompt encoders, mask decoder). We cannot use SamPredictor.predict (link) for two reasons: We want to fine-tune only the mask decoder This function calls SamPredictor.predict_torch which has the  @torch.no_grad() decorator (link), which prevents us from computing gradients Thus, we need to examine the SamPredictor.predict function and call the appropriate functions with gradient calculation enabled on the part we want to fine-tune (the mask decoder). Doing this is also a good way to learn more about how SAM works. Creating a Custom Dataset We need three things to fine-tune our model: Images on which to draw segmentations Segmentation ground truth masks Prompts to feed into the model We chose the stamp verification dataset (link) since it has data that SAM may not have seen in its training (i.e., stamps on documents). We can verify that it performs well, but not perfectly, on this dataset by running inference with the pre-trained weights. The ground truth masks are also extremely precise, which will allow us to calculate accurate losses. Finally, this dataset contains bounding boxes around the segmentation masks, which we can use as prompts to SAM. An example image is shown below. These bounding boxes align well with the workflow that a human annotator would go through when looking to generate segmentations. Input Data Preprocessing We need to preprocess the scans from numpy arrays to pytorch tensors. To do this, we can follow what happens inside SamPredictor.set_image (link) and SamPredictor.set_torch_image (link) which preprocesses the image. First, we can use utils.transform.ResizeLongestSide to resize the image, as this is the transformer used inside the predictor (link). We can then convert the image to a pytorch tensor and use the SAM preprocess method (link) to finish preprocessing. Training Setup We download the model checkpoint for the vit_b model and load them in: sam_model = sam_model_registry['vit_b'](checkpoint='sam_vit_b_01ec64.pth') We can set up an Adam optimizer with defaults and specify that the parameters to tune are those of the mask decoder: optimizer = torch.optim.Adam(sam_model.mask_decoder.parameters())  At the same time, we can set up our loss function, for example Mean Squared Error loss_fn = torch.nn.MSELoss() Training Loop In the main training loop, we will be iterating through our data items, generating masks, and comparing them to our ground truth masks so that we can optimize the model parameters based on the loss function. In this example, we used a GPU for training since it is much faster than using a CPU. It is important to use .to(device) on the appropriate tensors to make sure that we don’t have certain tensors on the CPU and others on the GPU. We want to embed images by wrapping the encoder in the torch.no_grad() context manager, since otherwise we will have memory issues, along with the fact that we are not looking to fine-tune the image encoder. with torch.no_grad(): image_embedding = sam_model.image_encoder(input_image) We can also generate the prompt embeddings within the no_grad context manager. We use our bounding box coordinates, converted to pytorch tensors. with torch.no_grad(): sparse_embeddings, dense_embeddings = sam_model.prompt_encoder( points=None, boxes=box_torch, masks=None, ) Finally, we can generate the masks. Note that here we are in single mask generation mode (in contrast to the 3 masks that are normally output). low_res_masks, iou_predictions = sam_model.mask_decoder( image_embeddings=image_embedding, image_pe=sam_model.prompt_encoder.get_dense_pe(), sparse_prompt_embeddings=sparse_embeddings, dense_prompt_embeddings=dense_embeddings, multimask_output=False, ) The final step here is to upscale the masks back to the original image size since they are low resolution. We can use Sam.postprocess_masks to achieve this. We will also want to generate binary masks from the predicted masks so that we can compare these to our ground truths. It is important to use torch functionals in order to not break backpropagation. upscaled_masks = sam_model.postprocess_masks(low_res_masks, input_size, original_image_size).to(device) from torch.nn.functional import threshold, normalize binary_mask = normalize(threshold(upscaled_masks, 0.0, 0)).to(device) Finally, we can calculate the loss and run an optimization step: loss = loss_fn(binary_mask, gt_binary_mask) optimizer.zero_grad() loss.backward() optimizer.step() By repeating this over a number of epochs and batches we can fine-tune the SAM decoder. Saving Checkpoints and Starting a Model from it Once we are done with training and satisfied with the performance uplift, we can save the state dict of the tuned model using: torch.save(model.state_dict(), PATH) We can then load this state dict when we want to perform inference on data that is similar to the data we used to fine-tune the model. {{light_callout_start}} You can find the Colab Notebook with all the code you need to fine-tune SAM here. Keep reading if you want a fully working solution out of the box! {{light_callout_end}} Fine-Tuning for Downstream Applications While SAM does not currently offer fine-tuning out of the box, we are building a custom fine-tuner integrated with the Encord platform. As shown in this post, we fine-tune the decoder in order to achieve this. This is available as an out-of-the-box one-click procedure in the web app, where the hyperparameters are automatically set. Original vanilla SAM mask: Mask generated by fine-tuned version of the model: We can see that this mask is tighter than the original mask. This was the result of fine-tuning on a small subset of images from the stamp verification dataset, and then running the tuned model on a previously unseen example. With further training and more examples, we could obtain even better results. Conclusion That's all, folks! You have now learned how to fine-tune the Segment Anything Model (SAM). If you're looking to fine-tune SAM out of the box, you might also be interested to learn that we have recently released the Segment Anything Model in Encord, allowing you to fine-tune the model without writing any code. {{SAM_CTA}}

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sampleImage_databricks-visualization
Visualizations in Databricks

With data becoming a pillar stone of a company’s growth strategy, the market for visualization tools is growing rapidly, with a projected compound annual growth rate (CAGR) of 10.07% between 2023 and 2028. The primary driver of these trends is the need for data-driven decision-making, which involves understanding complex data patterns and extracting actionable insights to improve operational efficiency.  PowerBI and Tableau are traditional tools with interactive workspaces for creating intuitive dashboards and exploring large datasets. However, other platforms are emerging to address the ever-changing nature of the modern data ecosystem. In this article, we will discuss the visualizations offered by Databricks - a modern enterprise-scale platform for building data, analytics, and artificial intelligence (AI) solutions. Databricks Databricks is an end-to-end data management and model development solution built on Apache Spark. It lets you create and deploy the latest generative AI (Gen AI) and large language models (LLMs). The platform uses a proprietary Mosaic AI framework to streamline the model development process. It provides tools to fine-tune LLMs seamlessly through enterprise data and offers a unified service for experimentation through foundation models. In addition, it features Databricks SQL, a state-of-the-art lakehouse for cost-effective data storage and retrieval. It lets you centrally store all your data assets in an open format, Delta Lake, for effective governance and discoverability. Further, Databricks SQL has built-in support for data visualization, which lets you extract insights from datasets directly from query results in the SQL editor. Users also benefit from the visualization tools featured in Databricks Notebooks, which help you build interactive charts by using the Plotly library in Python. Through these visualizations, Databricks offers robust data analysis for monitoring data assets critical to your AI models. So, let’s discuss in more detail the types of chart visualizations, graphs, diagrams, and maps available on Databricks to help you choose the most suitable visualization type for your use case. Effective visualization can help with effortless data curation. Learn more about how you can use data curation for computer vision Visualizations in Databricks As mentioned earlier, Databricks provides visualizations through Databricks SQL and Databricks Notebooks. The platform lets you run multiple SQL queries to perform relevant aggregations and apply filters to visualize datasets according to your needs. Databricks also allows you to configure settings related to the X and Y axes, legends, missing values, colors, and labels. Users can also download visualizations in PNG format for documentation purposes. The following sections provide an overview of the various visualization types available in these two frameworks, helping you select the most suitable option for your project. Bar Chart Bar charts are helpful when you want to compare the frequency of occurrence of different categories in your dataset. For instance, you can draw a bar chart to compare the frequency of various age groups, genders, ethnicities, etc. Additionally, bar charts can be used to view the sum of the prices of all orders placed in a particular month and group them by priority. Bar chart The result will show the months on the X-axis and the sum of all the orders categorized by priority on the Y-axis. Line Line charts connect different data points through straight lines. They are helpful when users want to analyze trends over some time. The charts usually show time on the X-axis and some metrics whose trajectory you want to explore on the Y-axis. Line chart For instance, you can view changes in the average price of orders over the years grouped by priority. The trends can help you predict the most likely future values, which can help you with financial projections and budget planning. Pie Chart Pie charts display the proportion of different categories in a dataset. They divide a circle into multiple segments, each showing the proportion of a particular category, with the segment size proportional to the category’s percentage of the total. Pie chart For instance, you can visualize the proportion of orders for each priority. The visualization is helpful when you want a quick overview of data distribution across different segments. It can help you analyze demographic patterns, market share of other products, budget allocation, etc. Scatter Plot A scatter plot displays each data point as a dot representing a relationship between two variables. Users can also control the color of each dot to reflect the relationship across different groups. Scatter Plot For instance, you can plot the relationship between quantity and price for different color-coded item categories. The visualization helps in understanding the correlation between two variables. However, users must interpret the relationship cautiously, as correlation does not always imply causation. Deeper statistical analysis is necessary to uncover causal factors. Area Charts Area charts combine line and bar charts by displaying lines and filling the area underneath with colors representing particular categories. They show how the contribution of a specific category changes relative to others over time. Area Charts For instance, you can visualize which type of order priority contributed the most to revenue by plotting the total price of different order priorities across time. The visualization helps you analyze the composition of a specific metric and how that composition varies over time. It is particularly beneficial in analyzing sales growth patterns for different products, as you can see which product contributed the most to growth across time. Box Chart Box charts concisely represent data distributions of numerical values for different categories. They show the distribution’s median, skewness, interquartile, and value ranges. Box Chart For instance, the box can display the median price value through a line inside the box and the interquartile range through the top and bottom box enclosures. The extended lines represent minimum and maximum price values to compute the price range. The chart helps determine the differences in distribution across multiple categories and lets you detect outliers. You can also see the variability in values across different categories and examine which category was the most stable. Bubble Chart Bubble charts enhance scatter plots by allowing you to visualize the relationship of three variables in a two-dimensional grid. The bubble position represents how the variable on the X-axis relates to the variable on the Y-axis. The bubble size represents the magnitude of a third variable, showing how it changes as the values of the first two variables change. Bubble chart The visualization is helpful for multi-dimensional datasets and provides greater insight when analyzing demographic data. However, like scatter plots, users must not mistake correlation for causation. Combo Chart Combo charts combine line and bar charts to represent key trends in continuous and categorical variables. The categorical variable is on the X-axis, while the continuous variable is on the Y-axis. Combo Chart For instance, you can analyze how the average price varies with the average quantity according to shipping date. The visualization helps summarize complex information involving relationships between three variables on a two-dimensional graph. However, unambiguous interpretation requires careful configuration of labels, colors, and legends. Heatmap Chart Heatmap charts represent data in a matrix format, with each cell having a different color according to the numerical value of a specific variable. The colors change according to the value intensity, with lower values typically having darker and higher values having lighter colors. Heatmap chart For instance, you can visualize how the average price varies according to order priority and order status. Heatmaps are particularly useful in analyzing correlation intensity between two variables. They also help detect outliers by representing unusual values through separate colors. However, interpreting the chart requires proper scaling to ensure colors do not misrepresent intensities. Histogram Histograms display the frequency of particular value ranges to show data distribution patterns. The X-axis contains the value ranges organized as bins, and the Y-axis shows the frequency of each bin. Histogram For instance, you can visualize the frequency of different price ranges to understand price distribution for your orders. The visualization lets you analyze data spread and skewness. It is beneficial in deeper statistical analysis, where you want to derive probabilities and build predictive models. Pivot Tables Pivot tables can help you manipulate tabular displays through drag-and-drop options by changing aggregation records. The option is an alternative to SQL filters for viewing aggregate values according to different conditions. Pivot Tables For instance, you can group total orders by shipping mode and order category. The visualization helps prepare ad-hoc reports and provides important summary information for decision-making. Interactive pivot tables also let users try different arrangements to reveal new insights. Choropleth Map Visualization Choropleth map visualization represents color-coded aggregations categorized according to different geographic locations. Regions with higher value intensities have darker colors, while those with lower intensities have lighter shades. Choropleth map visualization For instance, you can visualize the total revenue coming from different countries. This visualization helps determine global presence and highlight disparities across borders. The insights will allow you to develop marketing strategies tailored to regional tastes and behavior. Funnel Visualization Funnel visualization depicts data aggregations categorized according to specific steps in a pipeline. It represents each step from top to bottom with a bar and the associated value as a label overlay on each bar. It also displays cumulative percentage values showing the proportion of the aggregated value resulting from each stage. Funnel Visualization For instance, you can determine the incoming revenue streams at each stage of the ordering process. This visualization is particularly helpful in analyzing marketing pipelines for e-commerce sites. The tool shows the proportion of customers who view a product ad, click on it, add it to the cart, and proceed to check out. Cohort Analysis Cohort analysis offers an intuitive visualization to track the trajectory of a particular metric across different categories or cohorts. Cohort Analysis For instance, you can analyze the number of active users on an app that signed up in different months of the year. The rows will depict the months, and the columns will represent the proportion of active users in a particular cohort as they move along each month. The visualization helps in retention analysis as you can determine the proportion of retained customers across the user lifecycle. Counter Display Databricks allows you to configure a counter display that explicitly shows how the current value of a particular metric compares with the metric’s target value. Counter display For instance, you can check how the average total revenue compares against the target value. In Databricks, the first row represents the current value, and the second is the target. The visualization helps give a quick snapshot of trending performance and allows you to quantify goals for better strategizing. Sankey Diagrams Sankey diagrams show how data flows between different entities or categories. It represents flows through connected links representing the direction, with entities displayed as nodes on either side of a two-dimensional grid. The width of the connected links represents the magnitude of a particular value flowing from one entity to the other. Sankey Diagram For instance, you can analyze traffic flows from one location to the other. Sankey diagrams can help data engineering teams analyze data flows from different platforms or servers. The analysis can help identify bottlenecks, redundancies, and resource constraints for optimization planning. Sunburst Sequence The sunburst sequence visualizes hierarchical data through concentric circles. Each circle represents a level in the hierarchy and has multiple segments. Each segment represents the proportion of data in the hierarchy. Furthermore, it color codes segments to distinguish between categories within a particular hierarchy. Sunburst Sequence For instance, you can visualize the population of different world regions through a sunburst sequence. The innermost circle represents a continent, the middle one shows a particular region, and the outermost circle displays the country within that region. The visualization helps data science teams analyze relationships between nested data structures. The information will allow you to define clear data labels needed for model training. Table A table represents data in a structured format with rows and columns. Databricks offers additional functionality to hide, reformat, and reorder data. Tables help summarize information in structured datasets. You can use them for further analysis through SQL queries. Word Cloud Word cloud visualizations display words in different sizes according to their frequency in textual data. For instance, you can analyze customer comments or feedback and determine overall sentiment based on the highest-occurring words. Word Cloud While word clouds help identify key themes in unstructured textual datasets, they can suffer from oversimplification. Users must use word clouds only as a quick overview and augment textual analysis with advanced natural language processing techniques. Visualization is critical to efficient data management. Find out the top tools for data management for computer vision Visualizations in Databricks: Key Takeaways With an ever-increasing data volume and variety, visualization is becoming critical for quickly communicating data-based insights in a simplified manner. Databricks is a powerful tool with robust visualization types for analyzing complex datasets. Below are a few key points to remember regarding visualization in Databricks. Databricks SQL and Databricks Notebooks: Databricks offers advanced visualizations through Databricks SQL and Databricks Notebooks as a built-in functionality. Visualization configurations: Users can configure multiple visualization settings to produce charts, graphs, maps, and diagrams per their requirements. Visualization types: Databricks offers multiple visualizations, including bar charts, line graphs, pie charts, scatter plots, area graphs, box plots, bubble charts, combo charts, heatmaps, histograms, pivot tables, choropleth maps, funnels, cohort tables, counter display, Sankey diagrams, sunburst sequences, tables, and word clouds.

March 28

10 min

sampleImage_data-lake-guide
Data Lake Explained: A Comprehensive Guide for ML Teams

What is a Data Lake? A data lake is a centralized repository where you can store all your structured, semi-structured, and unstructured data types at any scale for processing, curation, and analytics. It supports batch and real-time streams to combine raw data from diverse sources (databases, IoT devices, mobile apps, etc.) into the repository without a predefined schema. It has been 12 years since the New York Times published an interesting article on ‘The Age of Big Data,’ in which most of the talk and tooling were centered around analytics. Fast-forward to today, and we are continuously grappling with the influx of data at the petabyte (PB) and zettabyte (ZB) scales, which is getting increasingly complex in dimensions (images, videos, point cloud data, etc.).  It is clear that solutions that can help manage the size and complexity of data are needed for organizational success. This has urged data, AI, and technology teams to look towards three pivotal data management solutions: data lakes, data warehouses, and cloud services. This article focuses on understanding data lakes as a data management solution for machine learning (ML) teams. You will learn: What a data lake is and how it differs from a data warehouse. Benefits and limitations of a data lake for ML teams. The data lake architecture. Best practices for setting up a data lake. On-premise vs. cloud-based data lakes. Computer vision use cases of data lakes.   TL; DR A data lake is a centralized repository for diverse, structured, and unstructured data. Key architecture components include Data Sources, Data Ingestion, Data Persistence and Storage, Data Processing Layer, Analytical Sandboxes, Data Lake Zones, and Data Consumption. Best practices for data lakes involve defining clear objectives, robust data governance, scalability, prioritizing security, encouraging a data-driven culture, and quality control. On-premises data lakes offer control and security; cloud-based data lakes provide scalability and cost efficiency. Data lakes are evolving with advanced analytics and computer vision use cases, emphasizing the need for adaptable systems and adopting forward-thinking strategies. Overview: Data Warehousing, Data Lake, and Cloud Storage Data Warehouses A data warehouse is a single location where an organization's structured data is consolidated, transformed, and stored for query and analysis. The structured data is ideal for generating reports and conducting analytics that inform business decisions. Limitations Limited agility in handling unstructured or semi-structured data. Can create data silos, hindering cross-departmental data sharing. Data Lakes A data lake stores vast amounts of raw datasets in their native format until needed, which includes structured, semi-structured, and unstructured data. This flexibility supports diverse applications, from computer vision use cases to real-time analytics. Challenges Risk of becoming a "data swamp" if not properly managed, with unclear, unclean, or redundant data. Requires robust metadata and governance practices to ensure data is findable and usable. Cloud Storage and Computing Cloud computing encompasses a broad spectrum of services beyond storage, such as processing power and advanced analytics. Cloud storage refers explicitly to storing data on the internet through a cloud computing provider that manages and operates data storage as a service. Risks Security concerns, requiring stringent data access controls and encryption. Potential for unexpected costs if usage is not monitored. Dependence on the service provider's reliability and continuity. Data lake overview with the data being ingested from different sources. Most ML teams misinterpret the role of data lakes and data warehouses, choosing an inappropriate management solution. Before delving into the rest of the article, let’s clarify how they differ. Data Lake vs. Data Warehouse Understanding the strengths and use cases of data lakes and warehouses can help your organization maximize its data assets. This can help create an efficient data infrastructure that supports various analytics, reporting, and ML needs. Let’s compare a data lake to a data warehouse based on specific features. Choosing Between Data Lake and Data Warehouse The choice between a data lake and a warehouse depends on the specific needs of the analysis. For an e-commerce organization analyzing structured sales data, a data warehouse offers the speed and efficiency required for such tasks.  However, a data lake (or a combination of both solutions) might be more appropriate for applications that require advanced computer vision (CV) techniques and large visual datasets (images, videos). Benefits of a Data Lake Data lakes offer myriad benefits to organizations using complex datasets for analytical insights, ML workloads, and operational efficiency. Here's an overview of the key benefits: Single Source of Truth: When you centralize data in data lakes, you get rid of data silos, which makes data more accessible across the whole organization. So, data lakes ensure that all the data in an organization is consistent and reliable by providing a single source of truth. Schema on Read: Unlike traditional databases that define data structure at write time (schema on write), data lakes allow the structure to be imposed at read time to offer flexibility in data analysis and utilization. Scalability and Cost-Effectiveness: Data lakes' cloud-based nature facilitates scalable storage solutions and computing resources, optimizing costs by reducing data duplication. Decoupling of Storage and Compute: Data lakes let different programs access the same data without being dependent on each other. This makes the system more flexible and helps it use its resources more efficiently. Architectural Principles for Data Lake Design When designing a data lake, consider these foundational principles: Decoupled Architecture: Data ingestion, processing, curation, and consumption should be independent to improve system resilience and adaptability. Tool Selection: Choose the appropriate tools and platforms based on data characteristics, ingestion, and processing requirements, avoiding a one-size-fits-all approach. Data Temperature Awareness: Classify data as hot (frequently accessed), warm (less frequently accessed), or cold (rarely accessed but retained for compliance) to optimize storage strategies and access patterns based on usage frequency. Leverage Managed Services: Use managed or serverless services to reduce operational overhead and focus on value-added activities. Immutability and Event Journaling: Design data lakes to be immutable, preserving historical data integrity and supporting comprehensive data analysis. They should also store and version the data labels. Cost-Conscious Design: Implement strategies (balancing performance, access needs, budget constraints) to manage and optimize costs without compromising data accessibility or functionality. Data Lake Architecture A robust data lake architecture is pivotal for harnessing the power of large datasets so organizations can store, process, and analyze them efficiently. This architecture typically comprises several layers dedicated to a specific function within the data management ecosystem. Below is an overview of these key components: Data Sources Diverse Producers: Data lakes can ingest data from a myriad of sources, including, but not limited to, IoT devices, cameras, weblogs, social media, mobile apps, transactional databases (SQL, NoSQL), and external APIs. This inclusivity enables a holistic view of business operations and customer interactions. Multiple Formats: They accommodate a wide range of data formats, from structured data in CSVs and databases to unstructured data like videos, images, DICOM files, documents, and multimedia files, providing a unified repository for all organizational data. This, of course, does not exclude semi-structured data like XML and JSON files. Data Ingestion Batch and Streaming: Data ingestion mechanisms in a data lake architecture support batch and real-time data flows. Use tools and services to auto-ingest the data so the system can effectively capture it. Validation and Metadata: Data is tagged with metadata during ingestion for easy retrieval, and initial validation checks are performed to ensure data quality and integrity. Data Governance Zone Access Control and Auditing: Implementing robust access controls, encryption, and auditing capabilities ensures data security and privacy, crucial for maintaining trust and compliance. Metadata Management: Documenting data origins, formats, lineage, ownership, and usage history is central to governance. This component incorporates tools for managing metadata, which facilitates data discovery, lineage tracking, and cataloging, enhancing the usability and governance of the data lake. Data Persistence and Staging Raw Data Storage: Data is initially stored in a staging area in raw, unprocessed form. This approach ensures that the original data is preserved for future processing needs and compliance requirements. Staging Area: Data may be staged or temporarily held in a dedicated area within the lake before processing. To efficiently handle the volume and variety of data, this area is built on scalable storage technologies, such as HDFS (Hadoop Distributed File System) or cloud-based storage services like Amazon S3. Data Processing Layer Transformation and Enrichment: This layer transforms data into a more usable format, often involving data cleaning, enrichment, deduplication, anonymization, normalization, and aggregation processes. It also improves data quality and ensures reliability for downstream analysis. Processing Engines: To cater to various processing needs, the architecture should support multiple processing engines, such as Hadoop for batch processing, Spark for in-memory processing, and others for specific tasks like stream processing. Data Indexing: This component indexes processed data to facilitate faster search and retrieval. It is crucial for supporting efficient data exploration and curation. Related: Interested in learning the techniques and best data cleaning and preprocessing practices? Check out one of our most-read guides, “Mastering Data Cleaning & Data Preprocessing.” Data Quality Monitoring Continuous Quality Checks: Implements automated processes for continuous monitoring of data quality, identifying issues like inconsistencies, duplications, or anomalies to maintain the accuracy, integrity, and reliability of the data lake. Quality Metrics and Alerts: Define and track data quality metrics, set up alert mechanisms for when data quality thresholds are breached, and enable proactive issue resolution. Related: Read how you can automate the assessment of training data quality in this article. Analytical Sandboxes Exploration and Experimentation: Computer vision engineers and data scientists can use analytical sandboxes to experiment with data sets, build models, and visually explore data (e.g., images, videos) and embeddings without impacting the integrity of the primary data (versioned data and labels). Tool Integration: These sandboxes support a wide range of analytics, data, and ML tools, giving users the flexibility and choice to work with their preferred technologies. Worth Noting: Building computer vision applications? Encord Active integrates with Annotate (with cloud platform integrations) and provides explorers with a way to explore image embeddings for any scale of data visually. See how to use it in the docs. Data Consumption Access and Integration: Data stored in the data lake is accessible to various downstream applications and users, including BI tools, reporting systems, computer vision platforms, or custom applications. This accessibility ensures that insights from the data lake can drive decision-making across the organization. APIs and Data Services: For programmatic access, APIs and data services enable developers and applications to query and retrieve data from the data lake, integrating data-driven insights into business processes and applications. Best Practices for Setting Up a Data Lake Implementing a data lake requires careful consideration and adherence to best practices to be successful and sustainable. Here are some suggested best practices to help you set up a data lake that can grow with your organization’s changing and growing data needs: #1. Define Clear Objectives and Scope Understand Your Data Needs: Before setting up a data lake, identify the types of data you plan to store, the insights you aim to derive, and the stakeholders who will consume this data. This understanding will guide your data lake's design, architecture, and governance model. Set Clear Objectives: Establish specific, measurable objectives for your data lake, such as improving data accessibility for analytics, supporting computer vision projects, or consolidating disparate data sources. These objectives will help prioritize features and guide decision-making throughout the setup process. #2. Ensure Robust Data Governance Implement a Data Governance Framework: A strong governance framework is essential for maintaining data quality, managing access controls, and ensuring compliance with regulatory standards. This framework should include data ingestion, storage, management, and archival policies. Metadata Management: Cataloging data with metadata is crucial for making it discoverable (indexing, filtering, sorting) and understandable. Implement tools and processes to automatically capture metadata, including data source, tags, format, and access permissions, during ingestion or at rest. Metadata can be technical (data design; schema, tables, formats, source documentation), business (docs on usage), and operational (events, access history, trace logs).   #3. Focus on Scalability and Flexibility Choose Scalable Infrastructure: Whether on-premises or cloud-based, ensure your data lake infrastructure can scale to accommodate future data growth without significant rework or additional investment. Plan for Varied Data Types: Design your data lake to handle structured, semi-structured, and unstructured data. Flexibility in storing and processing different data types (images, videos, DICOM, blob files, etc.) ensures the data lake can support a wide range of use cases. #4. Prioritize Security and Compliance Implement Strong Security Measures: Security is paramount for protecting sensitive data and maintaining user trust. Apply encryption in transit and at rest, manage access with role-based controls, and regularly audit data access and usage. Compliance and Data Privacy: Consider the legal and regulatory requirements relevant to your data. Incorporate compliance controls into your data lake's architecture and operations, including data retention policies and the right to be forgotten. #5. Foster a Data-Driven Culture Encourage Collaboration: Promote collaboration between software engineers, CV engineers, data scientists, and analysts to ensure the data lake meets the diverse needs of its users. Regular feedback loops can help refine and enhance the data lake's utility. Education and Training: Invest in stakeholder training to maximize the data lake's value. Understanding how to use the data lake effectively can spur innovation and lead to new insights across the organization. #6. Continuous Monitoring and Optimization Monitor Data Lake Health: Regularly monitor the data lake for performance, usage patterns, and data quality issues. This proactive approach can help identify and resolve problems before they impact users. Iterate and Optimize: Your organization's needs will evolve, and so will your data lake. Continuously assess its performance and utility, adjusting based on user feedback and changing business requirements. Cloud-based Data Lake Platforms Cloud-based data lake platforms offer scalable, flexible, and cost-effective solutions for storing and analyzing large amounts of data. These platforms provide Data Lake as a Service (DLaaS), which simplifies the setup and management of data lakes. This allows organizations to focus on deriving insights rather than infrastructure management.  Let's explore the architecture of data lake platforms provided by AWS, Azure, Snowflake, GCP, and their applications in multi-cloud environments. AWS Data Lake Architecture Amazon Web Services (AWS) provides a comprehensive and mature set of services to build a data lake. The core components include: Ingestion: AWS Glue for ETL processes and AWS Kinesis for real-time data streaming. Storage: Amazon S3 for scalable and secure data storage. Processing and Analysis: Amazon EMR is used for big data processing, AWS Glue for data preparation and loading, and Amazon Redshift for data warehousing. Consumption: Send your curated data to AWS SageMaker to run ML workloads or Amazon QuickSight to build visualizations, perform ad-hoc analysis, and quickly get business insights from data. Security and Governance: AWS Lake Formation automates the setup of a secure data lake, manages data access and permissions, and provides a centralized catalog for discovering and searching for data. Azure Data Lake Architecture Azure's data lake architecture is centered around Azure Data Lake Storage (ADLS) Gen2, which combines the capabilities of Azure Blob Storage and ADLS Gen1. It offers large-scale data storage with a hierarchical namespace and a secure HDFS-compatible data lake. Ingestion: Azure Data Factory for ETL operations and Azure Event Hubs for real-time event processing. Storage: ADLS Gen2 for a highly scalable data lake foundation.  Processing and Consumption: Azure Databricks for big data analytics running on Apache Spark, Azure Synapse Analytics for querying (SQL serverless) and analysis (Notebooks), and Azure HDInsight for Hadoop-based services. Power BI can connect to ADLS Gen2 directly to create interactive reports and dashboards. Security and Governance: Azure provides fine-grained access control with Azure Role-Based Access Control (RBAC) and secures data with Microsoft Entra ID. Snowflake Data Lake Architecture Snowflake's unique architecture separates compute and storage, allowing users to scale them independently. It offers a cloud-agnostic solution operating across AWS, Azure, and GCP. Ingestion: Within Snowflake, Snowpipe Streaming runs on top of Apache Kafka for real-time ingestion. Apache Kafka acts as the messaging broker between the source and Snowlake. You can run batch ingestion with Python scripts and the PUT command. Storage: Uses cloud provider's storage (S3, ADLS, or Google Cloud Storage) or internal (i.e., Snowflake) stages to store structured, unstructured, and semi-structured data in their native format. Processing and Curation: Snowflake's Virtual Warehouses provide dedicated compute resources for data processing for high performance and concurrency. Snowpark can implement business logic within existing programming languages. Data Sharing and Governance: Snowflake enables secure data sharing between Snowflake accounts with governance features for managing data access and security. Consumption: Snowflake provides native connectors for popular BI and data visualization tools, including Google Analytics and Looker. Snowflake Marketplace provides users access to a data marketplace to discover and access third-party data sets and services. Snowpark helps with features for end-to-end ML. High-level architecture for running data lake workloads using Snowpark in Snowflake Google Cloud Data Lake Architecture In addition to various processing and analysis services, Google Cloud Platform (GCP) bases its data lake solutions on Google Cloud Storage (GCS), the primary data storage service. Ingestion: Cloud Pub/Sub for real-time messaging Storage: GCS offers durable and highly available object storage. Processing: Cloud Data Fusion offers pre-built transformations for batch and real-time processing, and Dataflow is for serverless stream and batch data processing. Consumption and Analysis: BigQuery provides serverless, highly scalable data analysis with an SQL-like interface. Dataproc runs Apache Hadoop and Spark jobs. Vertex AI provides machine learning capabilities to analyze and derive insights from lake data. Security and Governance: Cloud Identity and Access Management (IAM) controls resource access, and Cloud Data Loss Prevention (DLP) helps discover and protect sensitive data. Data Lake Architecture on Multi-Cloud Multi-cloud data lake architectures leverage services from multiple cloud providers, optimizing for performance, cost, and regulatory compliance. This approach often involves: Cloud-Agnostic Storage Solutions: Storing data in a manner accessible across cloud environments, either through multi-cloud storage services or by replicating data across cloud providers. Cross-Cloud Services Integration: This involves using best-of-breed services from different cloud providers for ingestion, processing, analysis, and governance, facilitated by data integration and orchestration tools. Unified Management and Governance: Implement multi-cloud management platforms to ensure consistent monitoring, security, and governance across cloud environments. Implementing a multi-cloud data lake architecture requires careful planning and robust data management strategies to ensure seamless operation, data consistency, and compliance across cloud boundaries. On-Premises Data Lakes and Cloud-based Data Lakes Organizations looking to implement data lakes have two primary deployment models to consider: on-premises and cloud-based (although more recent approaches involve a hybrid of both solutions). Cost, scalability, security, and accessibility affect each model's advantages and disadvantages. On-Premises Data Lakes: Advantages Control and Security: On-premises data lakes offer organizations complete control over their infrastructure, which can be crucial for industries with stringent regulatory and compliance requirements. This control also includes data security, so security measures can be tailored to each organization's needs. Performance: With data stored locally, on-premises solutions can provide faster data access and processing speeds, which is beneficial for time-sensitive applications that require rapid data retrieval and analysis. On-Premises Data Lakes: Challenges Cost and Scalability: Establishing an on-premises data lake requires a significant upfront investment in hardware and infrastructure. Scaling up can also require additional hardware purchases and be time-consuming. Maintenance: On-premises data lakes necessitate ongoing maintenance, including hardware upgrades, software updates, and security patches, which require dedicated IT staff and resources. Cloud-based Data Lakes: Advantages Scalability and Flexibility: Cloud-based data lakes can change their storage and computing power based on changing data volumes and processing needs without changing hardware. Cost Efficiency: A pay-as-you-go pricing model allows organizations to avoid substantial upfront investments and only pay for their storage and computing resources, potentially reducing overall costs. Innovative Features: Cloud service providers always add new technologies and features to their services, giving businesses access to the most advanced data management and analytics tools. Cloud-based Data Lakes: Challenges Data Security and Privacy: While cloud providers implement robust security measures, organizations may have concerns about storing sensitive data off-premises, particularly in industries with strict data sovereignty regulations. Dependence on Internet Connectivity: Access to cloud-based data lakes relies on stable internet connectivity. Any disruptions in connectivity can affect data access and processing, impacting operations. Understanding these differences enables organizations to select the most appropriate data lake solution to support their data management strategy and business objectives. Computer Vision Use Cases of Data Lakes Data lakes are pivotal in powering computer vision applications across various industries by providing a scalable repository for storing and analyzing vast large image and video datasets in real-time. Here are some compelling use cases where data lakes improve computer vision applications: Healthcare: Medical Imaging and Diagnosis In healthcare, data lakes store vast collections of medical images (e.g., X-rays, MRIs, CT scans, PET) that, combined with data curation tools, can improve image quality, detect anomalies, and provide quantitative assessments. CV algorithms analyze these images in real time to diagnose diseases, monitor treatment progress, and plan surgeries. Case Study: Viz.ai uses artificial intelligence to speed care and improve patient outcomes. In this case study, learn how they ingest, annotate, curate, and consume medical data. Autonomous Vehicles: Navigation and Safety Autonomous vehicle developers use data lakes to ingest and curate diverse datasets from vehicle sensors, including cameras, LiDAR, and radar. This data is crucial for training computer vision algorithms that enable autonomous driving capabilities, such as object detection, automated curb management, traffic sign recognition, and pedestrian tracking. Case Study: Automotus builds real-time curbside management automation solutions. Learn how they ingested raw, unlabeled data into Encord via Annotate and curated a balanced, diverse dataset with Active in this case study. How Automotus increased mAP 20% by reducing their dataset size by 35% with visual data curation Agriculture: Precision Farming In the agricultural sector, data lakes store and curate visual data (images and videos) captured by drones or satellites over farmland. Computer vision techniques analyze this data to assess crop health, identify pest infestations, and evaluate water usage, so farmers can make informed decisions and apply treatments selectively. Case Study: Automated harvesting and analytics company Four Growers uses Encord’s platform and annotators to help build its training datasets from scratch, labeling millions of instances of greenhouses and plants. Learn how the platform has halved the time it takes for them to build training data in this case study. Security and Surveillance: Threat Detection Government and private security agencies use data lakes to compile video feeds from CCTV cameras in public spaces, airports, and critical infrastructure. Real-time analysis with computer vision helps detect suspicious activities, unattended objects, and unauthorized entries, triggering immediate responses to potential security threats. ML Team's Data Lake Guide: Key Takeaways Data lakes have become essential for scalable storage and processing of diverse data types in modern data management. They facilitate advanced analytics, including real-time applications like computer vision. Their ability to transform sectors ranging from finance to agriculture by enhancing operational efficiencies and providing actionable insights makes them invaluable. As we look ahead: The continuous evolution of data lake architectures, especially within cloud-native and multi-cloud contexts, promises to bring forth advanced tools and services for improved data handling. This progression presents an opportunity for enterprises to transition from viewing data lakes merely as data repositories to leveraging them as strategic assets capable of building advanced CV applications. To maximize data lakes, address the problems associated with data governance, security, and quality. This will ensure that data remains a valuable organizational asset and a catalyst for data-driven decision-making and strategy formulation.

March 28

11 min

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MM1: Apple’s Multimodal Large Language Models (MLLMs)

 What is MM1? MM1 is a family of large multimodal language models that combines text and image understanding. It boasts an impressive 30 billion parameters and excels in both pre-training and supervised fine-tuning. MM1 generates and interprets both images and text data, making it a powerful tool for various multimodal tasks. Additionally, it incorporates a mixture-of-experts (MoE) architecture, contributing to its state-of-the-art performance across benchmarks.  Introduction to Multimodal AI Multimodal AI models are a type of artificial intelligence model that can process and generate multiple types of data, such as text, images, and audio. These models are designed to understand the world in a way that is closer to how humans do, by integrating information from different modalities. Multimodal AI models typically use a combination of different types of AI systems, each designed to process a specific type of data. For example, a multimodal AI model might use a convolutional neural network (CNN) to process visual data, a recurrent neural network (RNN) to process text data, and a transformer model to integrate the information from CNN and RNN. The outputs of these networks are then combined, often using techniques such as concatenation or attention mechanisms, to produce a final output. This output can be used for a variety of tasks, such as classification, generation, or prediction. Overview of Multimodal Large Language Models (MLLMs) Multimodal Large Language Models (MLLMs) are generative AI systems that combine different types of information, such as text, images, videos, audio, and sensory data, to understand and generate human-like language. These models revolutionize the field of natural language processing (NLP) by going beyond text-only models and incorporating a wide range of modalities. Here's an overview of key aspects of Multimodal Large Language Models: Architecture MLLMs typically extend architectures like Transformers, which have proven highly effective in processing sequential data such as text. Transformers consist of attention mechanisms that enable the model to focus on relevant parts of the input data. In MLLMs, additional layers and mechanisms are added to process and incorporate information from other modalities. Integration of Modalities MLLMs are designed to handle inputs from multiple modalities simultaneously. For instance, they can analyze both the text and the accompanying image in a captioning task or generate a response based on both text and audio inputs. This integration allows MLLMs to understand and generate content that is richer and more contextually grounded. Pre-Training Like their unimodal counterparts, MLLMs are often pre-trained on large datasets using self-supervised learning objectives. Pre-training involves exposing the model to vast amounts of multimodal data, allowing it to learn representations that capture the relationships between different modalities. Pre-training is typically followed by fine-tuning on specific downstream tasks. State-of-the-Art Models CLIP (Contrastive Language-Image Pre-training): Developed by OpenAI, CLIP learns joint representations of images and text by contrasting semantically similar and dissimilar image-text pairs. GPT-4: It showcases remarkable capabilities in complex reasoning, advanced coding, and even performs well in multiple academic exams. Kosmos-1: Created by Microsoft, this MLLM os trained from scratch on web-scale multimodal corpora, including arbitrary interleaved text and images, image-caption pairs, and text data. PaLM-E: Developed by Google, PaLM-E integrates different modalities to enhance language understanding. Understanding MM1 Models MM1 represents a significant advancement in the domain of Multimodal Large Language Models (MLLMs), demonstrating state-of-the-art performance in pre-training metrics and competitive results in various multimodal benchmarks. The development of MM1 stems from a meticulous exploration of architecture components and data choices, aiming to distill essential design principles for building effective MLLMs. MM1 Model Experiments: Key Research Findings Architecture Components Image Encoder: The image encoder's design, along with factors such as image resolution and token count, significantly impacts MM1's performance. Through careful ablations, it was observed that optimizing the image encoder contributes substantially to MM1's capabilities. Vision-Language Connector: While important, the design of the vision-language connector was found to be of comparatively lesser significance compared to other architectural components. It plays a crucial role in facilitating communication between the visual and textual modalities. Data Choices Pre-training Data: MM1 leverages a diverse mix of image-caption, interleaved image-text, and text-only data for pre-training. This combination proved pivotal in achieving state-of-the-art few-shot results across multiple benchmarks. The study highlights the importance of different types of pre-training data for various tasks, with caption data being particularly impactful for zero-shot performance. Supervised Fine-Tuning (SFT): The effectiveness of pre-training data choices was validated through SFT, where capabilities and modeling decisions acquired during pre-training were retained, leading to competitive performance across evaluations and benchmarks. Performance In-Context Learning Abilities: The MM1 model exhibits exceptional in-context learning abilities, particularly in its largest 30 billion parameter configuration. This version of the model can perform multi-step reasoning over multiple images using few-shot “chain-of-thought” prompting. Model Scale: MM1's scalability is demonstrated through the exploration of larger LLMs, ranging from 3B to 30B parameters, and the investigation of mixture-of-experts (MoE) models. This scalability contributes to MM1's adaptability to diverse tasks and datasets, further enhancing its performance and applicability. Performance: The MM1 models, which include both dense models and mixture-of-experts (MoE) variants, achieve competitive performance after supervised fine-tuning on a range of established multimodal benchmarks. Apple MM1 Model’s Features In-Context Predictions The Apple MM1 model excels at making predictions within the context of a given input. By considering the surrounding information, it can generate more accurate and contextually relevant responses. For instance, when presented with a partial sentence or incomplete query, the MM1 model can intelligently infer the missing parts and provide meaningful answers. Multi-Image Reasoning The MM1 model demonstrates impressive capabilities in reasoning across multiple images. It can analyze and synthesize information from various visual inputs, allowing it to make informed decisions based on a broader context. For example, when evaluating a series of related images (such as frames from a video), the MM1 model can track objects, detect changes, and understand temporal relationships. Chain-of-Thought Reasoning One of the standout features of the MM1 model is its ability to maintain a coherent chain of thought. It can follow logical sequences, connect ideas, and provide consistent responses even in complex scenarios. For instance, when engaged in a conversation, the MM1 model remembers previous interactions and ensures continuity by referring back to relevant context. Few-Shot Learning with Instruction Tuning The MM1 model leverages few-shot learning techniques, enabling it to learn from a small amount of labeled data. Additionally, it fine-tunes its performance based on specific instructions, adapting to different tasks efficiently. For instance, if provided with only a handful of examples for a new task, the MM1 model can generalize and perform well without extensive training data. Visual Question Answering (VQA) The MM1 model can answer questions related to visual content through Visual Question Answering (VQA). Given an image and a question, it generates accurate and context-aware answers, demonstrating its robust understanding of visual information. For example, when asked, “What is the color of the car in the picture?” the MM1 model can analyze the image and provide an appropriate response. Captioning When presented with an image, the MM1 model can generate descriptive captions. Its ability to capture relevant details and convey them in natural language makes it valuable for image captioning tasks. For instance, if shown a picture of a serene mountain landscape, the MM1 model might generate a caption like, “Snow-capped peaks against a clear blue sky.” For more information, read the paper of Arxiv published by Apple researchers: MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training.   Key Components of MM1 Transformer Architecture The transformer architecture serves as the backbone of MM1. Self-Attention Mechanism: Transformers use self-attention to process sequences of data. This mechanism allows them to weigh the importance of different elements within a sequence, capturing context and relationships effectively. Layer Stacking: Multiple layers of self-attention are stacked to create a deep neural network. Each layer refines the representation of input data. Positional Encoding: Transformers incorporate positional information, ensuring they understand the order of elements in a sequence. Multimodal Pre-Training Data MM1 benefits from a diverse training dataset: Image-Text Pairs: These pairs directly connect visual content (images) with corresponding textual descriptions. The model learns to associate the two modalities. Interleaved Documents: Combining images and text coherently allows MM1 to handle multimodal inputs seamlessly. Text-Only Data: Ensuring robust language understanding, even when dealing with text alone. Image Encoder The image encoder is pivotal for MM1’s performance: Feature Extraction: The image encoder processes visual input (images) and extracts relevant features. These features serve as the bridge between the visual and textual modalities. Resolution and Token Count: Design choices related to image resolution and token count significantly impact MM1’s ability to handle visual information. Vision-Language Connector The vision-language connector facilitates communication between textual and visual representations: Cross-Modal Interaction: It enables MM1 to align information from both modalities effectively. Joint Embeddings: The connector generates joint embeddings that capture shared semantics. Ablation Study for MLLMs Building performant Multimodal Large Language Models (MLLMs) is an empirical process that involves carefully exploring various design decisions related to architecture, data, and training procedures. Here, the authors present a detailed ablation study conducted to identify optimal configurations for constructing a high-performing model, referred to as MM1. The ablations are performed along three major axes: MM1 Model Ablations Different pre-trained image encoders are investigated, along with various methods of connecting Large Language Models (LLMs) with these encoders. The architecture exploration encompasses the examination of the image encoder pre-training objective, image resolution, and the design of the vision-language connector. MM1 Model Ablation MM1 Data Ablations Various types of data and their relative mixture weights are considered, including captioned images, interleaved image-text documents, and text-only data. The impact of different data sources on zero-shot and few-shot performance across multiple captioning and Visual Question Answering (VQA) tasks is evaluated. Data Ablation Study for MM1 Training Procedure Ablations The training procedure is explored, including hyperparameters and which parts of the model to train at different stages. Two types of losses are considered: contrastive losses (e.g., CLIP-style models) and reconstructive losses (e.g., AIM), with their effects on downstream performance examined. Empirical Setup A smaller base configuration of the MM1 model is used for ablations, allowing for efficient assessment of model performance. The base configuration includes an Image Encoder (ViT-L/14 model trained with CLIP loss on DFN-5B and VeCap-300M datasets), Vision-Language Connector (C-Abstractor with 144 image tokens), Pre-training Data (mix of captioned images, interleaved image-text documents, and text-only data), and a 1.2B transformer decoder-only Language Model. Zero-shot and few-shot (4- and 8-shot) performance on various captioning and VQA tasks are used as evaluation metrics. MM1 Ablation Study: Key Findings Image resolution, model size, and training data composition are identified as crucial factors affecting model performance. The number of visual tokens and image resolution significantly impact the performance of the Vision-Language Connector, while the type of connector has a minimal effect. Interleaved data is crucial for few-shot and text-only performance, while captioning data enhances zero-shot performance. Text-only data helps improve few-shot and text-only performance, contributing to better language understanding capabilities. Careful mixture of image and text data leads to optimal multimodal performance while retaining strong text performance. Synthetic caption data (VeCap) provides a notable boost in few-shot learning performance. Performance Evaluation of MM1 Models The performance evaluation of MM1 models encompasses several key aspects, including scaling via Mixture-of-Experts (MoE), supervised fine-tuning (SFT) experiments, impact of image resolution, pre-training effects, and qualitative analysis. Scaling via Mixture-of-Experts (MoE) MM1 explores scaling the dense model by incorporating more experts in the Feed-Forward Network (FFN) layers of the language model. Two MoE models are designed: 3B-MoE with 64 experts and 7B-MoE with 32 experts, utilizing top-2 gating and router z-loss terms for training stability. The MoE models demonstrate improved performance over their dense counterparts across various benchmarks, indicating the potential of MoE for further scaling. Supervised Fine-Tuning Experiments Supervised Fine-Tuning (SFT) is performed on top of the pre-trained MM1 models using a diverse set of datasets, including instruction-response pairs, academic task-oriented vision-language datasets, and text-only data. MM1 models exhibit competitive performance across 12 benchmarks, showing particularly strong results on tasks such as VQAv2, TextVQA, ScienceQA, and newer benchmarks like MMMU and MathVista. The models maintain multi-image reasoning capabilities even during SFT, enabling few-shot chain-of-thought reasoning. Impact of Image Resolution Higher image resolution leads to improved performance, supported by methods such as positional embedding interpolation and sub-image decomposition. MM1 achieves a relative performance increase of 15% by supporting an image resolution of 1344×1344 compared to a baseline model with an image resolution of 336 pixels. Pre-Training Effects Large-scale multimodal pre-training significantly contributes to the model's performance improvement over time, showcasing the importance of pre-training data quantity. MM1 demonstrates strong in-context few-shot learning and multi-image reasoning capabilities, indicating the effectiveness of large-scale pre-training for enhancing model capabilities. Qualitative Analysis Qualitative examples provided in the evaluation offer further insights into MM1's capabilities, including single-image and multi-image reasoning, as well as few-shot prompting scenarios. These examples highlight the model's ability to understand and generate contextually relevant responses across various tasks and input modalities. Apple’s Ethical Guidelines for MM1 Privacy and Data Security: Apple places utmost importance on user privacy. MM1 models are designed to respect user data and adhere to strict privacy policies. Any data used for training is anonymized and aggregated. Bias Mitigation: Apple actively works to reduce biases in MM1 models. Rigorous testing and monitoring are conducted to identify and rectify any biases related to gender, race, or other sensitive attributes. Transparency: Apple aims to be transparent about the capabilities and limitations of MM1. Users should have a clear understanding of how the model works and what it can and cannot do. Fairness: MM1 is trained on diverse data, but Apple continues to improve fairness by addressing underrepresented groups and ensuring equitable outcomes. Safety and Harm Avoidance: MM1 is designed to avoid harmful or unsafe behavior. It refrains from generating content that could cause harm, promote violence, or violate ethical norms. Human Oversight: Apple maintains a strong human-in-the-loop approach. MM1 models are continuously monitored, and any problematic outputs are flagged for review. MM1 MLLM: Key Takeaways Multimodal Integration: MM1 combines textual and visual information, achieving impressive performance. Ablation Study Insights: Image encoder matters, connector less so. Data mix is crucial. Scaling Up MM1: Up to 30 billion parameters, strong pre-training metrics, competitive fine-tuning. Ethical Guidelines: Privacy, fairness, safety, and human oversight are priorities.

March 26

10 min

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Microsoft MORA: Multi-Agent Video Generation Framework

What is Mora? Mora is a multi-agent framework designed for generalist video generation. Based on OpenAI's Sora, it aims to replicate and expand the range of generalist video generation tasks. Sora, famous for making very realistic and creative scenes from written instructions, set a new standard for creating videos that are up to a minute long and closely match the text descriptions given. Mora distinguishes itself by incorporating several advanced visual AI agents into a cohesive system. This lets it undertake various video generation tasks, including text-to-video generation, text-conditional image-to-video generation, extending generated videos, video-to-video editing, connecting videos, and simulating digital worlds. Mora can mimic Sora’s capabilities using multiple visual agents, significantly contributing to video generation. In this article, you will learn: Mora's innovative multi-agent framework for video generation. The importance of open-source collaboration that Mora enables. Mora's approach to complex video generation tasks and instruction fidelity. About the challenges in video dataset curation and quality enhancement. TL; DR Mora's novel approach uses multiple specialized AI agents, each handling different aspects of the video generation process. This innovation allows various video generation tasks, showcasing adaptability in creating detailed and dynamic video content from textual descriptions. Mora aims to fix the problems with current models like Sora, which is closed-source and does not let anyone else use it or do more research in the field, even though it has amazing text-to-video conversion abilities 📝🎬. Unfortunately, Mora still has problems with dataset quality, video fidelity, and ensuring that outputs align with complicated instructions and people's preferences. These problems show where more work needs to be done in the future. OpenAI Sora’s Closed-Source Nature The closed-source nature of OpenAI's Sora presents a significant challenge to the academic and research communities interested in video generation technologies. Sora's impressive capabilities in generating realistic and detailed videos from text descriptions have set a new standard in the field.   Related: New to Sora? Check out our detailed explainer on the architecture, relevance, limitations, and applications of Sora.   However, the inability to access its source code or detailed architecture hinders external efforts to replicate or extend its functionalities. This limits researchers from fully understanding or replicating its state-of-the-art performance in video generation.  Here are the key challenges highlighted due to Sora's closed-source nature: Inaccessibility to Reverse-Engineer Without access to Sora's source code, algorithms, and detailed methodology, the research community faces substantial obstacles in dissecting and understanding the underlying mechanisms that drive its exceptional performance.  This lack of transparency makes it difficult for other researchers to learn from and build upon Sora's advancements, potentially slowing down the pace of innovation in video generation. Extensive Training Datasets Sora's performance is not just the result of sophisticated modeling and algorithms; it also benefits from training on extensive and diverse datasets. But the fact that researchers cannot get their hands on similar datasets makes it very hard to copy or improve Sora's work. High-quality, large-scale video datasets are crucial for training generative models, especially those capable of creating detailed, realistic videos from text descriptions. However, these datasets are often difficult to compile due to copyright issues, the sheer volume of data required, and the need for diverse, representative samples of the real world. Creating, curating, and maintaining high-quality video datasets requires significant resources, including copyright permissions, data storage, and management capabilities. Sora's closed nature worsens these challenges by not providing insights into compiling the datasets, leaving researchers to navigate these obstacles independently. Computational Power Creating and training models like Sora require significant computational resources, often involving large clusters of high-end GPUs or TPUs running for extended periods. Many researchers and institutions cannot afford this much computing power, which makes the gap between open-source projects like Mora and proprietary models like Sora even bigger. Without comparable computational resources, it becomes challenging to undertake the necessary experimentation—with different architectures and hyperparameters—and training regimes required to achieve similar breakthroughs in video generation technology. Learn more about these limitations in the technical paper.   Evolution: Text-to-Video Generation Over the years, significant advancements in text-to-video generation technology have occurred, with each approach and architecture uniquely contributing to the field's growth.  Here's a summary of these evolutionary stages, as highlighted in the discussion about text-to-video generation in the Mora paper: GANs (Generative Adversarial Networks) Early attempts at video generation leveraged GANs, which consist of two competing networks: a generator that creates images or videos that aim to be indistinguishable from real ones, and a discriminator that tries to differentiate between the real and generated outputs. Despite their success in image generation, GANs faced challenges in video generation due to the added complexity of temporal coherence and higher-dimensional data. Generative Video Models Moving beyond GANs, the field saw the development of generative video models designed to produce dynamic sequences. Generating realistic videos frame-by-frame and maintaining temporal consistency is a challenge, unlike in static image generation. Auto-Regressive Transformers Auto-regressive transformers were a big step forward because they could generate video sequences frame-by-frame. These models predicted each new frame based on the previously generated frames, introducing a sequential element that mirrors the temporal progression of videos. But this approach often struggled with long-term coherence over longer sequences. Large-Scale Diffusion Models Diffusion models, known for their capacity to generate high-quality images, were extended to video generation. These models gradually refine a random noise distribution toward a coherent output. They apply this iterative denoising process to the temporal domain of videos. Related: Read our guide on HuggingFace’s Dual-Stream Diffusion Net for Text-to-Video Generation. Image Diffusion U-Net Adapting the U-Net architecture for image diffusion models to video content was critical. This approach extended the principles of image generation to videos, using a U-Net that operates over sequences of frames to maintain spatial and temporal coherence. 3D U-Net Structure The change to a 3D U-Net structure allowed for more nuance in handling video data, considering the extra temporal dimension. This change also made it easier to model time-dependent changes, improving how we generate coherent and dynamic video content. Latent Diffusion Models (LDMs) LDMs generate content in a latent space rather than directly in pixel space. This approach reduces computational costs and allows for more efficient handling of high-dimensional video data. LDMs have shown that they can better capture the complex dynamics of video content. Diffusion Transformers Diffusion transformers (DiT) combine the strengths of transformers in handling sequential data with the generative capabilities of diffusion models. This results in high-quality video outputs that are visually compelling and temporally consistent.  Useful: Stable Diffusion 3 is an example of a multimodal diffusion transformer model that generates high-quality images and videos from text. Check out our explainer on how it works. AI Agents: Advanced Collaborative Multi-agent Structures The paper highlights the critical role of collaborative, multi-agent structures in developing Mora. It emphasizes their efficacy in handling multimodal tasks and improving video generation capabilities.  Here's a concise overview based on the paper's discussion on AI Agents and their collaborative frameworks: Multimodal Tasks Advanced collaborative multi-agent structures address multimodal tasks involving processing and generating complex data across different modes, such as text, images, and videos. These structures help integrate various AI agents, each specialized in handling specific aspects of the video generation process, from understanding textual prompts to creating visually coherent sequences. Cooperative Agent Framework (Role-Playing) The cooperative agent framework, characterized by role-playing, is central to the operation of these multi-agent structures. Each agent is assigned a unique role or function in this framework, such as prompt enhancement, image generation, or video editing.  By defining these roles, the framework ensures that an agent with the best skills for each task is in charge of that step in the video generation process, increasing overall efficiency and output quality. Multi-Agent Collaboration Strategy The multi-agent collaboration strategy emphasizes the orchestrated interaction between agents to achieve a common goal. In Mora, this strategy involves the sequential and sometimes parallel processing of tasks by various agents. For instance, one agent might enhance an initial text prompt, convert it into another image, and finally transform it into a video sequence by yet another. This collaborative approach allows for the flexible and dynamic generation of video content that aligns with user prompts. AutoGen (Generic Programming Framework) A notable example of multi-agent collaboration in practice is AutoGen. This generic programming framework is designed to automate the assembly and coordination of multiple AI agents for a wide range of applications.  Within the context of video generation, AutoGen can streamline the configuration of agents according to the specific requirements of each video generation task to generate complex video content from textual or image-based prompts. Mora drone to butterfly flythrough shot. | Image Source. Role of an AI Agent The paper outlines the architecture involving multiple AI agents, each serving a specific role in the video generation process. Here's a closer look at the role of each AI agent within the framework:   Illustration of how to use Mora to conduct video-related tasks Prompt Selection and Generation Agent This agent is tasked with processing and optimizing textual prompts for other agents to process them further. Here are the key techniques used for Mora: GPT-4: This agent uses the generative capabilities of GPT-4 to generate high-quality prompts that are detailed and rich in context. Prompt Selection: This involves selecting or enhancing textual prompts to ensure they are optimally prepared for the subsequent video generation process. This step is crucial for setting the stage for generating images and videos that closely align with the user's intent. Good Read: Interested in GPT-4 Vision alternatives? Check out our blog post. Text-to-Image Generation Agent This agent uses a retrained large text-to-image model to convert the prompts into initial images. The retraining process ensures the model is finely tuned to produce high-quality images, laying a strong foundation for the video generation process. Image-to-Image Generation Agent  This agent specializes in image-to-image generation, taking initial images and editing them based on new prompts or instructions. This ability allows for a high degree of customization and improvement in video creation. Image-to-Video Generation Agent This agent transforms static images into dynamic video sequences, extending the visual narrative by generating coherent frames. Here are the core techniques and models: Core Components: It incorporates two pre-trained models: GPT-3 for understanding and generating text-based instructions, and Stable Diffusion for translating these instructions into visual content. Prompt-to-Prompt Technique: The prompt-to-prompt technique guides the transformation from an initial image to a series of images that form a video sequence. Classifier-Free Guidance: Classifier-free guidance is used to improve the fidelity of generated videos to the textual prompts so that the videos remain true to the users' vision. Text-to-Video Generation Agent: This role is pivotal in transforming static images into dynamic videos that capture the essence of the provided descriptions. Stable Video Diffusion (SVD) and Hierarchical Training Strategy: A model specifically trained to understand and generate video content, using a hierarchical training strategy to improve the quality and coherence of the generated videos. Video Connection Agent This agent creates seamless transitions between two distinct video sequences for a coherent narrative flow. Here are the key techniques used: Pre-Trained Diffusion-Based T2V Model: This model uses a pre-trained diffusion-based model specialized in text-to-video (T2V) tasks to connect separate video clips into a cohesive narrative. Text-Based Control: This method uses textual descriptions to guide the generation of transition videos that seamlessly connect disparate video clips, ensuring logical progression and thematic consistency. Image-to-Video Animation and Autoregressive Video Prediction: These capabilities allow the agent to animate still images into video sequences, predict and generate future video frames based on previous sequences, and create extended and coherent video narratives. Mora’s Video Generation Process Mora's video-generation method is a complex, multi-step process that uses the unique capabilities of specialized AI agents within its framework. This process allows Mora to tackle various video generation tasks, from creating videos from text descriptions to editing and connecting existing videos.  Here's an overview of how Mora handles each task: Mora’s video generation process. Text-to-Video Generation This task begins with a detailed textual prompt from the user. Then, the Text-to-Image Generation Agent converts the prompts into initial static images. These images serve as the basis for the Image-to-Video Generation Agent, which creates dynamic sequences that encapsulate the essence of the original text and produce a coherent video narrative. Text-Conditional Image-to-Video Generation This task combines textual prompts with a specific starting image. Mora first improves the input with the Prompt Selection and Generation Agent, ensuring that the text and image are optimally prepared for video generation.  Then, the Image-to-Video Generation Agent takes over, generating a video that evolves from the initial image and aligns with the textual description. Extend Generated Videos To extend an existing video, Mora uses the final frame of the input video as a launchpad. The Image-to-Video Generation Agent crafts additional sequences that logically continue the narrative from the last frame, extending the video while maintaining narrative and visual continuity. Video-to-Video Editing In this task, Mora edits existing videos based on new textual prompts. The Image-to-Image Generation Agent first edits the video's initial frame according to the new instructions. Then, the Image-to-Video Generation Agent generates a new video sequence from the edited frame, adding the desired changes to the video content. Connect Videos Connecting two videos involves creating a transition between them. Mora uses the Video Connection Agent, which analyzes the first video's final frame and the second's initial frame. It then generates a transition video that smoothly links the two segments into a cohesive narrative flow. Simulating Digital Worlds Mora generates video sequences in this task that simulate digital or virtual environments. The process involves appending specific style cues (e.g., "in digital world style") to the textual prompt, guiding the Image-to-Video Generation Agent to create a sequence reflecting the aesthetics of a digital realm.  This can involve stylistically transforming real-world images into digital representations or generating new content within the specified digital style. See Also: Read our explainer on Google’s Video Gaming Companion: Scalable Instructable Multiworld Agent [SIMA].   Mora: Experimental Setup As detailed in the paper, the experimental setup for evaluating Mora is comprehensive and methodically designed to assess the framework's performance across various dimensions of video generation. Here's a breakdown of the setup: Baseline The baseline for comparison includes existing open-sourced models that showcase competitive performance in video generation tasks. These models include Videocrafter, Show-1, Pika, Gen-2, ModelScope, LaVie-Interpolation, LaVie, and CogVideo.  These models are a reference point for evaluating Mora's advancements and position relative to the current state-of-the-art video generation. Basic Metrics The evaluation framework comprises several metrics to quantify Mora's performance across different dimensions of video quality and condition consistency: Video Quality Measurement Object Consistency: Measures the stability of object appearances across video frames. Background Consistency: Assesses the uniformity of the background throughout the video. Motion Smoothness: Evaluates the fluidity of motion within the video. Aesthetic Score: Gauges the artistic and visual appeal of the video. Dynamic Degree: Quantifies the video's dynamic action or movement level. Imaging Quality: Assesses the overall visual quality of the video, including clarity and resolution. Video Condition Consistency Metric Temporal Style: Measures how consistently the video reflects the temporal aspects (e.g., pacing, progression) described in the textual prompt. Appearance Style: Evaluates the adherence of the video's visual style to the descriptions provided in the prompt, ensuring that the generated content matches the intended appearance. Self-Defined Metrics Video-Text Integration (VideoTI): Measures the model’s fidelity to textual instructions by comparing text representations of input images and generated videos. Temporal Consistency (TCON): Evaluates the coherence between an original video and its extended version, providing a metric for assessing the integrity of extended video content. Temporal Coherence (Tmean): Quantifies the correlation between the intermediate generated and input videos, measuring overall temporal coherence. Video Length: This parameter quantifies the duration of the generated video content, indicating the model's capacity for producing videos of varying lengths. Implementation Details The experiments use high-performance hardware, specifically TESLA A100 GPUs with substantial VRAM. This setup ensures that Mora and the baseline models are evaluated under conditions allowing them to fully express their video generation capabilities. The choice of hardware reflects the computational intensity of training and evaluating state-of-the-art video generation models. Mora video generation - Fish underwater flythrough Limitations of Mora The paper outlines several limitations of the Mora framework. Here's a summary of these key points: Curating High-Quality Video Datasets Access to high-quality video datasets is a major challenge for training advanced video generation models like Mora. Copyright restrictions and the sheer volume of data required make it difficult to curate diverse and representative datasets that can train models capable of generating realistic and varied video content. Read Also: The Full Guide to Video Annotation for Computer Vision.   Quality and Length Gaps While Mora demonstrates impressive capabilities, it has a noticeable gap in quality and maximum video length compared to state-of-the-art models like Sora. This limitation is particularly evident in tasks requiring the generation of longer videos, where maintaining visual quality and coherence becomes increasingly challenging. Simulating videos in Mora vs in Sora. Instruction Following Capability Mora sometimes struggles to precisely follow complex or detailed instructions, especially when generating videos that require specific actions, movements, or directionality. This limitation suggests that further improvement in understanding and interpreting textual prompts is needed. Human Visual Preference Alignment The experimental results may not always align with human visual preferences, particularly in scenarios requiring the generation of realistic human movements or the seamless connection of video segments. This misalignment highlights the need to incorporate a more nuanced understanding of physical laws and human dynamics into the video-generation process. Mora Vs. Sora: Feature Comparisons The paper compares Mora and OpenAI's Sora across various video generation tasks. Here's a detailed feature comparison based on their capabilities in different aspects of video generation: Check out the project repository on GitHub. Mora Multi-Agent Framework: Key Takeaways The paper "Mora: Enabling Generalist Video Generation via a Multi-Agent Framework" describes Mora, a new framework that advances video technology. Using a multi-agent approach, Mora is flexible and adaptable across various video generation tasks, from creating detailed scenes to simulating complex digital worlds. Because it is open source, it encourages collaboration, which leads to new ideas, and lets the wider research community add to and improve its features. Even though Mora has some good qualities, it needs high-quality video datasets, video quality, length gaps, trouble following complicated instructions correctly, and trouble matching outputs to how people like to see things. Finding solutions to these problems is necessary to make Mora work better and be used in more situations.  Continuing to improve and develop Mora could change how we make video content so it is easier for creators and viewers to access and have an impact.

March 26

8 min

sampleImage_9-best-image-annotation-tools-for-computer-vision
Best Image Annotation Tools for Computer Vision [Updated 2024]

Guide to the most popular image annotation tools that you need to know about in 2024. Compare the features and pricing, and choose the best image annotation tool for your use case. It’s 2024—annotating images is still one of the most time-consuming steps in bringing a computer vision project to market. To help you out, we put together a list of the most popular image labeling tools out there. Whether you are: A computer vision team building unmanned drones with your own in-house annotation tool. A team of data scientists working on an autonomous driving project looking for large-scale labeling services. Or a data operations team working in healthcare looking for the right platform for your radiologists to accurately label CT scans. This guide will help you compare the top AI annotation tools and find the right one for you. We will compare each based on key factors - including image annotation service, support for different data types and use cases, QA/QC capabilities, security and data privacy, integration with the machine learning pipeline, and customer support. But first, let's explore the process of selecting an image annotation tool from the available providers. Choosing the right image annotation tool is a critical decision that can significantly impact the quality and efficiency of the annotation process. To make an informed choice, it's essential to consider several factors and evaluate the suitability of an image annotation tool for specific needs. Evaluating Image Annotation Tools for Computer Vision Projects Selecting the perfect image annotation tool is like choosing the perfect brush for your painting. Different projects require specific annotation needs that dictate how downstream components. When evaluating an annotation tool that fits your project specifications, there are a few key factors you have to consider. In this section, we will explore those key factors and practical considerations to help you navigate the selection process and find the most fitting AI annotation tool for your computer vision applications. Annotation Types: An effective labeling tool should support various annotation types, such as bounding boxes (ideal for object localization), polygons (useful for detailed object outlines), keypoints (for pose estimation), and semantic segmentation (for scene understanding). The tool must be adaptable to different annotation requirements, allowing users to annotate images with precision and specificity based on the task at hand. User Interface (UI) and User Experience (UX): The user interface plays a crucial role in the efficiency and accuracy of the annotation process. A good annotation tool should have an intuitive interface that is easy to navigate, reducing the learning curve for users. Clear instructions, user-friendly controls, and efficient workflows contribute to a smoother annotation experience. Scalability: Consider the tool's ability to scale with the growing volume of data. A tool that efficiently handles large datasets and multiple annotators is crucial for projects with evolving requirements. Automation and AI Integration: Look for image labeling tools that offer automation features, such as automatic annotation tools or features, to accelerate the annotation process. Integration with artificial intelligence (AI) algorithms can further enhance efficiency by automating repetitive tasks, reducing manual effort, and improving annotation accuracy. Collaboration and Workflow Management: Assess the data annotation tool's collaboration features, including version control, user roles, and workflow management. Collaboration tools are essential for teams working on complex annotation projects. Data Security and Privacy: Ensure that the tool adheres to data security and privacy standards like GDPR. Evaluate encryption methods, access controls, and policies regarding the handling of sensitive data. Pricing: Consider various pricing models, such as per-user, per-project, or subscription models. Also factor in scalability costs, and potential additional fees, ensuring transparency in the pricing structure. Once you've identified which factors are most important for you to evaluate image annotating tools, the next step is understanding how to assess their suitability for your specific use case.  Most Popular Image Annotation Tools Let's compare the features offered by the best image annotation companies such as Encord, Scale AI, Label Studio, SuperAnnotate, CVAT, and Amazon SageMaker Ground Truth, and understand how they assist in annotating images. This article discusses the top 17 image annotation tools in 2024 to help you choose the right image annotation software for your use case. Encord Scale CVAT Label Studio Labelbox Playment Appen Dataloop SuperAnnotate V7 Labs Hive COCO Annotator Make Sense VGG Image Annotator LabelMe Amazon SageMaker Ground Truth VOTT Encord Encord is an automated annotation platform for AI-assisted image annotation, video annotation, and dataset management.  Key Features Data Management: Compile your raw data into curated datasets, organize datasets into folders, and send datasets for labeling.  AI-assisted Labeling: Automate 97% of your annotations with 99% accuracy using auto-annotation features powered by Meta's Segment Anything Model or GPT-4’s LLaVA. Collaboration: Integrate human-in-the-loop seamlessly with customized Workflows - create workflows with the no-code drag and drop builder to fit your data ops & ML pipelines. Quality Assurance: Robust annotator management & QA workflows to track annotator performance and increase label quality.  Integrated Data Labeling Services for all Industries: outsource your labeling tasks to an expert workforce of vetted, trained and specialized annotators to help you scale. Video Labeling Tool: provides the same support for video annotation. One of the leading video annotation tools with positive customer reviews, providing automated video annotations without frame rate errors. Robust Security Functionality: label audit trails, encryption, FDA, CE Compliance, and HIPAA compliance. Integrations: Advanced Python SDK and API access (+ easy export into JSON and COCO formats). Best for Commercial teams: Teams translating from an in-house solution or open-source tool that require a scalable annotation workflow with a robust, secure, and collaborative enterprise-grade platform. Complex or unique use case: For teams that require advanced annotation tool and functionality. It includes, complex nested ontologies or rendering native DICOM formats. Pricing Simple per-user pricing – no need to track annotation hours, label consumption or data usage.    Curious? Try it out Scale Scale AI, now Scale, is a data and labeling services platform that supports computer vision use cases but specializes in RLHF, user experience optimization, large language models, and synthetic data. Scale AI's Image Annotation Tool Key Features Customizable Workflows: Offers customizable labeling workflows tailored to specific project requirements and use cases. Data labeling services: Provides high-quality data labeling services for various data types, including images, text, audio, and video. Scalability: Capable of handling large-scale annotation projects and accommodating growing datasets and annotation needs. Best for Teams Looking for a Labeling Tool: Scale is a very popular option for data labeling services. Teams Looking for Annotation Tools for Autonomous Vehicle Vision: Scale is one of the earliest platforms on the market to support 3D Sensor Fusion annotation for RADAR and LiDAR use cases. Teams Looking for Medical Imaging Annotation Tools: Platforms like Scale will usually not support DICOM or NIfTI data types nor allow companies to work with their data annotators on the platform. Pricing On a per-image basis CVAT (Computer Vision Annotation Tool) CVAT is an open source image annotation tool that is a web-based annotation toolkit, built by Intel. For image labeling, CVAT supports four types of annotations: points, polygons, bounding boxes, and polylines, as well as a subset of computer vision tasks: image segmentation, object detection, and image classification. In 2022, CVAT’s data, content, and GitHub repository were migrated over to OpenCV, where CVAT continues to be open-source. Furthermore, CVAT can also be utilized to annotate QR codes within images, facilitating the integration of QR code recognition into computer vision pipelines and applications. CVAT Label Editor Key Features Open-source: Easy and free to get started labeling images. Manual Annotation Tools: Supports a wide range of annotation types including bounding boxes, polygons, polylines, points, and cuboids, catering to diverse annotation needs. Multi-platform Compatibility: Works on various operating systems such as Windows, Linux, and macOS, providing flexibility for users. Export Formats: CVAT offers support for various data formats including JSON, COCO, and XML-based like Pascal VOC, ensuring annotation compatibility with diverse tools and platforms. Best for Students, researchers, and academics testing the waters with image annotation (perhaps with a few images or a small dataset). Not preferable for commercial teams as it lacks scalability, collaborative features, and robust security. Pricing Free 💡 More insights on image labeling with CVAT: For a team looking for free image annotation tools, CVAT is one of the most popular open-source tools in the space, with over 1 million downloads since 2021. Other popular free image annotation alternatives to CVAT are 3D Slicer, Labelimg, VoTT (Visual Object Tagging Tool - developed by Microsoft), VIA (VGG Image Annotator), LabelMe, and Label Studio. If data security is a requirement for your annotation project… Commercial labeling tools will most likely be a better fit — key security features like audit trails, encryption, SSO, and generally-required vendor certifications (like SOC2, HIPAA, FDA, and GDPR) are usually not available in open-source tools. Further reading: Overview of open source annotation tools for computer vision Complete guide to image annotation for computer vision    Label Studio Label Studio is another popular open source data labeling platform. It provides a versatile platform for annotating various data types, including images, text, audio, and video. Label Studio supports collaborative labeling, custom labeling interfaces, and integration with machine learning pipelines for data annotation tasks. Label Studio Image Annotation Tool Key Features Customizable Labeling Interfaces: Flexible configuration for tailored annotation interfaces to specific tasks. Collaboration Tools: Real-time annotation and project sharing capabilities for seamless collaboration among annotators. Extensible: Easily connect to cloud object storage and label data there directly Export Formats: Label Studio supports multiple data formats including JSON, CSV, TSV, and VOC XML like Pascal VOC, facilitating integration and annotation from diverse sources for machine learning tasks. Best for Data scientists, machine learning engineers, and researchers or teams requiring versatile data labeling for images.  Not suitable for teams with limited technical expertise or resources for managing an open source tool Price Free with enterprise plan available Labelbox Labelbox is a US-based data annotation platform founded in 2017. Like most of the other platforms mentioned in this guide, Labelbox offers both an image labeling platform, as well as labeling services. Labelbox Image Editor Key Features Data Management: QA workflows and data annotator performance tracking. Customizable Labeling Interface: 3rd party labeling services through Labelbox Boost. Automation: Integration with AI models for automatic data labeling to accelerate the annotation process. Annotation Type: Support for multiple data types beyond images, especially text. Best for Teams looking for a platform to quickly annotate documents and text. Teams carrying out annotation projects that are use-case specific. As generalist tools, platforms like Labelbox are great at handling a broad variety of data types. If you’re working on a unique use-case-specific annotation project (like scans in DICOM formats or high-resolution images that require pixel-perfect annotations), other commercial AI labeling tools will be a better fit: check out our blog exploring Best DICOM Labeling Tools. Pricing Varies based on the volume of data, percent of the total volume needing to be labeled, number of seats, number of projects, and percent of data used in model training. For larger commercial teams, this pricing may get expensive as your project scales. Playment Playment is a fully-managed data annotation platform. The workforce labeling company was acquired by Telus in 2021 and provides computer vision teams with training data for various use cases, supported by manual labelers and a machine learning platform. Playment Image Annotation Tool Key Features Data Labeling Services: Provides high-quality data labeling services for various data types including images, videos, text, and sensor data. Support: Global workforces of contractors and data labelers. Scalability: Capable of handling large-scale annotation projects and accommodating growing datasets and annotation needs. Audio Labeling Tool: Speech recognition training platform (handles all data types across 500+ languages and dialects). Best for Teams looking for a fully managed solution who do not need visibility into the process. Pricing Enterprise plan Appen Appen is a data labeling services platform founded in 1996, making it one of the first and oldest solutions in the market. The company offers data labeling services for a wide range of industries and in 2019, acquired Figure Eight to build out its software capabilities and help businesses also train and improve their computer vision models. Appen Image Annotation Tool Key Features Data Labeling Services: Support for multiple annotation types (bounding boxes, polygons, and image segmentation). Data Collection: Data sourcing (pre-labeled datasets), data preparation, and real-world model evaluation. Natural Language Processing:  Supports natural language processing tasks such as sentiment analysis, entity recognition, and text classification. Image and Video Analysis: Analyzes images and videos for tasks such as object detection, image classification, and video segmentation. Best for Teams looking for image data sourcing and collection alongside annotation services. Pricing Enterprise plan Dataloop Dataloop is an Israel-based data labeling platform that provides a comprehensive solution for data Dataloop is an Israel-based data labeling platform that provides a comprehensive solution for data management and annotation projects. The tool offers data labeling capabilities across images, text, audio, and video annotation, helping businesses train and improve their machine learning models. Dataloop Image Annotation Tool Key Features Data Annotation: Features for image annotation tasks, including classification, detection, and semantic segmentation. Video Annotation Tool: Support for video annotations. Collaboration Tool: Features for real-time collaboration among annotators, project sharing, and version control for efficient teamwork. Data Management: Offers data management capabilities including data versioning, tracking, and organization for streamlined workflows. Best for Teams looking for a generalist annotation tool for various data annotation needs. Teams carrying out specific image and video annotation projects that are use-case specific. As generalist tools, platforms like Dataloop are built to support a wide variety of simple use cases, so other commercial platforms are a better fit if you’re trying to label use-case-specific annotation projects (like high-resolution images that require pixel-perfect annotations in satellite imaging or DICOM files for medical teams). Pricing Free trial and an enterprise plan. SuperAnnotate SuperAnnotate provides enterprise solutions for image and video annotation, catering primarily to the needs of the computer vision community. It provides powerful annotation tools and features tailored for machine learning and AI applications, offering efficient labeling solutions to enhance model training and accuracy. SuperAnnotate - Image Annotation Tool Key Features Multi-Data Type Support: Versatile annotation tool for image, video, text, and audio. AI Assistance: Integrates AI-assisted annotation to accelerate the annotation process and improve efficiency. Customization: Provides customizable annotation interfaces and workflows to tailor annotation tasks according to specific project requirements. Integration: Seamlessly integrates with machine learning pipelines and workflows for efficient model training and deployment. Scalability: Capable of handling large-scale annotation projects and accommodating growing datasets and annotation needs. Export Formats: SuperAnnotate supports multiple data formats, including popular ones like JSON, COCO, and Pascal VOC. Best for Larger teams working on various machine learning solutions looking for a versatile annotation tool. Pricing Free for early stage startups and academics for team size up to 3. Enterprise plan V7 Labs V7 is a UK-based data annotation platform founded in 2018. The company enables teams to annotate training data, support the human-in-the-loop processes, and also connect with annotation services. V7 offers annotation of a wide range of data types alongside image annotation tooling, including documents and videos. V7 Labs Image Annotation Tool Key Features Collaboration Capabilities: Project management and automation workflow functionality, with real-time collaboration and tagging. Data Labeling Services: Provides labeling services for images and videos. AI Assistance: Model-assisted annotation of multiple annotation types (segmentation, detection, and more). Best for Students or teams looking for a generalist platform to easily annotate different data types in one place (like documents, images, and short videos). Limited functionalities for use-case specific annotations. Pricing Various options, including academic, business, and pro. Hive Hive was founded in 2013 and provides cloud-based AI solutions for companies wanting to label content across a wide range of data types, including images, video, audio, text, and more. Hive Image Annotation Tool Key Features Image Annotation Tool: Offers annotation tools and workflows for labeling images along with support for unique image annotation use cases (ad targeting, semi-automated logo detection). Ease of Access: Flexible access to model predictions with a single API call. Integration: Seamlessly integrates with machine learning pipelines and workflows for AI model training and deployment. Best for Teams labeling images and other data types for the purpose of content moderation. Pricing Enterprise plan COCO Annotator COCO Annotator is a web-based image annotation tool, crafted by Justin Brooks under the MIT license. Specifically designed to streamline the process of labeling images for object detection, localization, and keypoints detection models, this tool offers a range of features that cater to the diverse needs of machine learning practitioners and researchers.  COCO Annotator - Image Annotation Tool Key Features Image Annotation: Supports annotation of images for object detection, instance segmentation, keypoint detection, and captioning tasks. Export Formats: To facilitate large-scale object detection, the tool exports and stores annotations in the COCO format.  Automations: The tool makes annotating an image easier by incorporating semi-trained models. Additionally, it provides access to advanced selection tools, including the MaskRCNN, Magic Wand and DEXTR. Best For ML Research Teams: COCO Annotator is a good choice for ML researchers, preferable for image annotation for tasks like object detection and keypoints detection. Price Free Make Sense Make Sense AI is a user-friendly and open-source annotation tool, available under the GPLv3 license. Accessible through a web browser without the need for advanced installations, this tool simplifies the annotation process for various image types. Make Sense - Image Annotation Tool Key Features Open Sourced: Make Sense AI stands out as an open-source tool, freely available under the GPLv3 license, fostering collaboration and community engagement for its ongoing development. Accessibility: It ensures web-based accessibility, operating seamlessly in a web browser without complex installations, promoting ease of use across various devices. Export Formats: It facilitates exporting annotations in multiple formats (YOLO, VOC XML like Pascal VOC, VGG JSON, and CSV), ensuring compatibility with diverse machine learning algorithms and seamless integration into various workflows. Best For Small teams seeking an efficient solution to annotate an image. Price Free VGG Image Annotator VGG Image Annotator (VIA) is a versatile open-source tool crafted by the Visual Geometry Group (VGG) for the manual annotation of both image and video data. Released under the permissive BSD-2 clause license, VIA serves the needs of both academic and commercial users, offering a lightweight and accessible solution for annotation tasks. VGG Image Annotator - Image Annotation Tool Key Features Lightweight and User-Friendly: VIA is a lightweight, self-contained annotation tool, utilizing HTML, Javascript, and CSS without external libraries, enabling offline usage in modern web browsers without setup or installation. Offline Capability: The tool is designed to be used offline, providing a full application experience within a single HTML file of size less than 200 KB.  Multi-User Collaboration: Facilitates collaboration among multiple annotators with features such as project sharing, real-time annotation, and version control. Best For VGG Image Annotator (VIA) is ideal for individuals and small teams involved in projects for academic researchers. Price Free LabelMe LabelMe is an open-source web-based tool developed by the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) that allows users to label and annotate images for computer vision research. It provides a user-friendly interface for drawing bounding boxes, polygons, and semantic segmentation masks to label objects within images. LabelMe Image Annotation Tool Key Features Web-Based: Accessible through a web-based interface, allowing for annotation tasks to be performed in any modern web browser without requiring software installation. Customizable Interface: Provides a customizable annotation interface with options to adjust settings, colors, and layout preferences to suit specific project requirements. Best for Academic and research purposes Pricing Free Amazon SageMaker Ground Truth Amazon SageMaker Ground Truth is a fully managed data labeling service provided by Amazon Web Services (AWS). It offers a platform for efficiently labeling large datasets to train machine learning models. Ground Truth supports various annotation tasks, including image classification, object detection, semantic segmentation, and more. Amazon SageMaker Ground Truth - Image Annotation Tool Key Features Managed Service: Fully managed by AWS, eliminating the need for infrastructure setup and management. Human-in-the-Loop Labeling: Harnesses the power of human feedback across the ML lifecycle to improve the accuracy and relevancy of models. Scalability: Capable of handling large-scale annotation projects and accommodating growing datasets and annotation needs. Integration with Amazon SageMaker: Seamlessly integrates with Amazon SageMaker for model training and deployment, providing a streamlined end-to-end machine learning workflow. Best for Teams requiring large-scale data labeling. Pricing Varies based on labeling task and type of data. VOTT VOTT or Visual Object Tagging Tool is an open-source tool developed by Microsoft for annotating images and videos to create training datasets for computer vision models. VOTT provides an intuitive interface for drawing bounding boxes around objects of interest and labeling them with corresponding class names. VOTT Image Annotation Tool Key Features Versatile Annotation Tool: Supports a wide range of annotation types including bounding boxes, polygons, polylines, points, and segmentation masks for precise labeling. Video Annotation: Enables annotation of videos frame by frame, with support for object tracking and interpolation to streamline the annotation process. Multi-Platform Compatibility: Works across various operating systems such as Windows, Linux, and macOS, ensuring flexibility for users. Best for Teams requiring lightweight and customizable annotation tool for object detection. Pricing Free Image Annotation Tool: Key Takeaways There you have it! The 17 Best Image Annotation Tools for computer vision in 2024.  For further reading, you might also want to check out a few 2024 honorable mentions, both paid and free annotation tools: Supervisely - commercial data labeling platform praised for its quality control functionality and basic interpolation feature. Labelimg - Labelimg is an open source multi-modal data annotation tool now part of Label Studio. MarkUp - MarkUp image is a free web annotation tool to annotate an image or a PDF.

March 26

10 min

sampleImage_automated-segmentation-tracking-for-video
Announcing Auto-Segmentation Tracking For Video

In computer vision, where accurate training data is the lifeblood of successful models, video annotation plays an important role. However, annotating each frame individually is time-consuming and prone to inconsistencies. Nearby frames often exhibit visual similarities, and annotations made on one frame can be extrapolated to others. Enter automated polygon and bitmask tracking! Automated segmentation tracking significantly reduces annotation time, while simultaneously improving accuracy - gone are the days of tediously labeling every frame in a video. Polygon and Bitmask tracking provides the tooling required to build labeled training data at scale and at speed. Polygon tracking meticulously outlines objects with a series of interconnected vertices, offering precision and flexibility unparalleled in video annotation. Conversely, Bitmask tracking simplifies the annotation process by representing object masks as binary images, streamlining efficiency without compromising clarity. Join us as we explore these techniques that are not just enhancing the process of video annotation, but also paving the way for more accurate and efficient machine learning models. 🚀 Understanding Polygon and Bitmask Tracking Polygon Tracking A polygon is a geometric shape defined by a closed loop of straight-line segments. It can have three or more sides, forming a boundary around an area. In video annotation, polygons are used to outline objects of interest within frames. By connecting a series of vertices, we create a polygon that encapsulates the object’s shape. Advantages of Polygon-Based Tracking Accurate Boundary Representation: Polygons provide a precise representation of an object’s boundary. Unlike bounding boxes (which are rectangular and may not align perfectly with irregular shapes), polygons can closely follow the contours of complex objects. Flexibility: Polygons are versatile. They can adapt to various object shapes, including non-rectangular ones. Whether you’re tracking a car, a person, or an animal, polygons allow for flexibility in annotation. Use Cases of Polygon Tracking Object Segmentation: When segmenting objects from the background, polygons excel. For instance, in medical imaging, they help delineate tumors or organs. Motion Analysis: Tracking moving objects often involves polygon-based annotation. Analyzing the trajectory of a soccer ball during a match or monitoring pedestrian movement in surveillance videos are examples. Bitmask Tracking A bitmask is a binary image where each pixel corresponds to a specific object label. Instead of outlining the object’s boundary, bitmasks assign a unique value (usually an integer) to each pixel within the object region. These values act as identifiers, allowing pixel-level annotation. Advantages of Bitmask-Based Tracking Bitmasks enable precise delineation at the pixel level. By assigning values to individual pixels, we achieve accurate object boundaries. This is especially useful when dealing with intricate shapes or fine details. Use Cases of Bitmask Tracking Semantic Segmentation: In semantic segmentation tasks, where the goal is to classify each pixel into predefined classes (e.g., road, sky, trees), bitmasks play a vital role. They provide ground truth labels for training deep learning models. Instance Segmentation: For scenarios where multiple instances of the same object class appear in a frame (e.g., identifying individual cars in a traffic scene), bitmask tracking ensures each instance is uniquely labeled. Temporal Consistency Maintaining temporal consistency when annotating objects in a video is crucial. This means that the annotations for an object should be consistent from one frame to the next. Inconsistent annotations can lead to inaccurate results when the annotated data is used for training machine learning models. Temporal smoothing and interpolation techniques can be used to improve the consistency of the tracking. Temporal smoothing involves averaging the annotations over several frames to reduce the impact of any sudden changes. Interpolation, on the other hand, involves estimating the annotations for missing frames based on the annotations of surrounding frames. Both these techniques can greatly improve the quality and consistency Read the documentation, to know how to use interpolation in your annotation.   Applications of Polygon and Bitmask Tracking Object Detection and Tracking With polygon tracking, objects of any shape can be accurately annotated, making it particularly useful for tracking objects that have irregular shapes or change shape over time. Bitmask tracking takes this a step further by marking each individual pixel, capturing even the smallest details of the object. This level of precision is crucial for detecting and tracking objects accurately within a video. Semantic Segmentation In semantic segmentation, the goal is to classify each pixel in the image to a particular class, making it a highly detailed task. Bitmask tracking, with its ability to mark each individual pixel, is perfectly suited for this task. It allows for the creation of highly accurate masks that can be used to train models for semantic segmentation. Polygon tracking can also be used for semantic segmentation, especially in scenarios where the objects being segmented have clear, defined boundaries. Interactive Video Editing Interactive video editing is a process where users can manipulate and modify video content. This involves tasks such as object removal, color grading, and adding special effects. Polygon and bitmask tracking can greatly enhance the process of interactive video editing. With these techniques, objects within the video can be accurately tracked and annotated, making it easier to apply edits consistently across multiple frames. This can lead to more seamless and high-quality edits, improving the overall video editing process. Semantic Context and Automation Semantic Context Scene Understanding: When placing polygons or bitmasks for video annotation, it’s crucial to consider the context of the scene. The semantics of the scene can guide accurate annotations. For instance, understanding the environment, the objects present, and their spatial relationships can help in placing more accurate and meaningful annotations. Object Relationships: The way objects interact within a scene significantly affects annotation choices. Interactions such as occlusion (where one object partially or fully hides another) and containment (where one object is inside another) need to be considered. Understanding these relationships can lead to more accurate and contextually relevant annotations. Automated Annotation Tool AI Assitance: With the advancement of machine learning models, we now have the capability to propose initial annotations automatically. These AI tools can significantly reduce the manual effort required in the annotation process. They can quickly analyze a video frame and suggest potential annotations based on learned patterns and features. Human Refinement: While AI tools can propose initial annotations, human annotators are still needed to refine these automated results for precision. Annotators can correct any inaccuracies and add nuances that the AI might have missed. This combination of AI assistance and human refinement leads to a more efficient and accurate video annotation process. Read the blog The Full Guide to Automated Data Annotation for more information.   Real-World Applications Polygon and Bitmask tracking, along with the concepts of semantic context and automation, have a wide range of real-world applications. Here are a few key areas where they are making a significant impact: Medical Imaging: In medical imaging, precise annotation can mean the difference between a correct and incorrect diagnosis. These techniques allow for highly accurate segmentation of medical images, which can aid in identifying and diagnosing a wide range of medical conditions. Autonomous Vehicles: Polygon and Bitmask tracking allow these vehicles to understand their environment in great detail, helping them make better driving decisions. Video Surveillance: In video surveillance, tracking objects accurately over time is key to identifying potential security threats. These techniques can improve the accuracy and efficiency of video surveillance systems, making our environments safer. These are just a few examples of the many possible applications of Polygon and Bitmask tracking. As these techniques continue to evolve, they are set to revolutionize numerous industries and fields. In summary, Polygon and Bitmask tracking are transforming video annotation, paving the way for more precise machine learning models. As we continue to innovate in this space, we’re excited to announce that Encord will be releasing new features soon. Stay tuned for these updates and join us in exploring the future of computer vision with Encord. 🚀

March 22

5 min

sampleImage_yolo-object-detection-guide
YOLO Real-time Object Detection: A Beginner’s Guide

What is YOLO Object Detection? YOLO (You Only Look Once) models are real-time object detection systems that identify and classify objects in a single pass of the image. What is Object Detection? Object detection is a critical capability of computer vision that identifies and locates objects within an image or video. Unlike image classification, object detection not only classifies the objects in an image, but also identifies their location within the image by drawing a bounding box around each object. Object detection models, such as R-CNN, Fast R-CNN, Faster R-CNN, and YOLO, use convolutional neural networks (CNNs) to classify the objects and regressor networks to accurately predict the bounding box coordinates for each detected object. Image Classification Image classification is a fundamental task in computer vision. Given an input image, the goal of an image classification model is to assign it to one of a pre-defined set of classes. Most image classification models use CNNs, which are specifically designed to process pixel data and can capture spatial features. Image classification models are trained on large datasets (like ImageNet) and can classify images into 1000 object categories, such as keyboard, mouse, pencil, and many animals. Object Localization Object localization is another important task in computer vision that identifies the location of an object in the image. It extends the image classification model by adding a regression head to predict the bounding box coordinates of the object. The bounding box is typically represented by four coordinates that define its position and size. Object localization is a key step in object detection, where the goal is not just to classify the primary object of interest in the image, but also to identify its location. Classification of Object Detection Algorithms Object detection algorithms can be broadly classified into two categories: single-shot detectors and two-shot(or multi-shot) detectors. These two types of algorithms have different approaches to the task of object detection. Single-shot Object Detection Single-shot detectors (SSDs) are a type of object detection algorithm that predict the bounding box and the class of the object in one single shot. This means that in a single forward pass of the network, the presence of an object and the bounding box are predicted simultaneously. This makes SSDs very fast and efficient, suitable for tasks that require real-time detection. Structure of SSD Examples of single-shot object detection algorithms include YOLO (You Only Look Once) and SSD (Single Shot MultiBox Detector). YOLO divides the input image into a grid and for each grid cell, predicts a certain number of bounding boxes and class probabilities. SSD, on the other hand, predicts bounding boxes and class probabilities at multiple scales in different feature maps. Two-shot Object Detection Two-shot or multi-shot object detection algorithms, on the other hand, use a two-step process for detecting objects. The first step involves proposing a series of bounding boxes that could potentially contain an object. This is often done using a method called region proposal. The second step involves running these proposed regions through a convolutional neural network to classify the object classes within the box. Examples of two-shot object detection algorithms include R-CNN (Regions with CNN features), Fast R-CNN, and Faster R-CNN. These algorithms use region proposal networks (RPNs) to propose potential bounding boxes and then use CNNs to classify the proposed regions. Both single-shot and two-shot detectors have their strengths and weaknesses. Single-shot detectors are generally faster and more efficient, making them suitable for real-time object detection tasks. Two-shot detectors, while slower and more computationally intensive, tend to be more accurate, as they can afford to spend more computational resources on each potential object. Object Detection Methods Object Detection: Non-Neural Methods Viola-Jones object detection method based on Haar features The Viola-Jones method, introduced by Paul Viola and Michael Jones, is a machine learning model for object detection. It uses a cascade of classifiers, selecting features from Haar-like feature sets. The algorithm has four stages: Haar Feature Selection Creating an Integral Image Adaboost Training Cascading Classifiers Despite its simplicity and speed, it can achieve high detection rates. Scale-Invariant Feature Transform (SIFT) SIFT is a method for extracting distinctive invariant features from images. These features are invariant to image scale and rotation, and are robust to changes in viewpoint, noise, and illumination. SIFT features are used to match different views of an object or scene. Histogram of Oriented Gradients (HOG) HOG is a feature descriptor used for object detection in computer vision. It involves counting the occurrences of gradient orientation in localized portions of an image. This method is similar to edge orientation histograms, scale-invariant feature transform descriptors, and shape contexts, but differs in that it is computed on a dense grid of uniformly spaced cells. Object Detection: Neural Methods Region-Based Convolutional Neural Networks (R-CNN) Region-Based CNN uses convolutional neural networks to classify image regions in order to detect objects. It involves training a CNN on a large labeled dataset and then using the trained network to detect objects in new images. Region-Based CNN and its successors, Fast R-CNN and Faster R-CNN, are known for their accuracy but can be computationally intensive. Faster R-CNN Faster R-CNN is an advanced version of R-CNN that introduces a Region Proposal Network (RPN) for generating region proposals. The RPN shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. The RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. Faster R-CNN is faster than the original R-CNN and Fast R-CNN because it doesn’t need to run a separate region proposal method on the image, which can be slow. Mask R-CNN Mask R-CNN extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. This allows Mask R-CNN to generate precise segmentation masks for each detected object, in addition to the class label and bounding box. The mask branch is a small fully convolutional network applied to each RoI, predicting a binary mask for each RoI. Mask R-CNN is simple to train and adds only a small computational overhead, enabling a fast system and rapid experimentation. Single Shot Detector (SSD) SSD is a method for object detection that eliminates the need for multiple network passes for multiple scales. It discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location. SSD is faster than methods like R-CNN because it eliminates bounding box proposals and pooling layers. RetinaNet RetinaNet uses a feature pyramid network on top of a backbone to detect objects at different scales and aspect ratios. It introduces a new loss, the Focal Loss, to deal with the foreground-background class imbalance problem. RetinaNet is designed to handle dense and small objects. EfficientDet EfficientDet is a method that scales all dimensions of the network width, depth, and resolution with a compound scaling method to achieve better performance. It introduces a new architecture, called BiFPN, which allows easy and efficient multi-scale feature fusion, and a new scaling method that uniformly scales the resolution, depth, and width for all backbone, feature network, and box/class prediction networks at the same time. EfficientDet achieves state-of-the-art accuracy with fewer parameters and less computation compared to previous detectors. You Only Look Once (YOLO) YOLO, developed by Joseph Redmon et al., frames object detection as a regression problem to spatially separated bounding boxes and associated class probabilities. It looks at the whole image at test time so its predictions are informed by global context in the image. YOLO is known for its speed, making it suitable for real-time applications. You Only Look Once: Unified, Real-Time Object Detection Performance Evaluation Metrics for Object Detection Intersection over Union (IoU) Intersection over Union (IoU) is a common metric used to evaluate the performance of an object detection algorithm. It measures the overlap between the predicted bounding box (P) and the ground truth bounding box (G). The IoU is calculated as the area of intersection divided by the area of union of P and G. The IoU score ranges from 0 to 1, where 0 indicates no overlap and 1 indicates a perfect match. A higher IoU score indicates a more accurate object detection. Average Precision (AP) Average Precision (AP) is another important metric used in object detection. It summarizes the precision-recall curve that is created by varying the detection threshold. Precision is the proportion of true positive detections among all positive detections, while recall is the proportion of true positive detections among all actual positives in the image. The AP computes the average precision values for recall levels over 0 to 1. The AP score ranges from 0 to 1, where a higher value indicates better performance. The mean Average Precision (mAP) is often used in practice, which calculates the AP for each class and then takes the average. By understanding these metrics, we can better interpret the performance of models like YOLO and make informed decisions about their application in real-world scenarios.  After exploring various object detection methods and performance evaluation methods, let’s delve into the workings of a particularly powerful and popular algorithm known as ‘You Only Look Once’, or YOLO. This algorithm has revolutionized the field of object detection with its unique approach and impressive speed. Unlike traditional methods that involve separate steps for identifying objects and classifying them, YOLO accomplishes both tasks in a single pass, hence the name ‘You Only Look Once’.  YOLO Algorithm: How Does it Work? YOLO Architecture The YOLO algorithm employs a single Convolutional Neural Network (CNN) that divides the image into a grid. Each cell in the grid predicts a certain number of bounding boxes. Along with each bounding box, the cell also predicts a class probability, which indicates the likelihood of a specific object being present in the box. YOLO Structure Bounding Box Recognition Process The bounding box recognition process in YOLO involves the following steps: Grid Creation: The image is divided into an SxS grid. Each grid cell is responsible for predicting an object if the object’s center falls within it. Bounding Box Prediction: Each grid cell predicts B bounding boxes and confidence scores for those boxes. The confidence score reflects how certain the model is that a box contains an object and how accurate it thinks the box is. Class Probability Prediction: Each grid cell also predicts C conditional class probabilities (one per class for the potential objects). These probabilities are conditioned on there being an object in the box. Non-Max Suppression (NMS) After the bounding boxes and class probabilities are predicted, post-processing steps are applied. One such step is Non-Max Suppression (NMS). NMS helps in reducing the number of overlapping bounding boxes. It works by eliminating bounding boxes that have a high overlap with the box that has the highest confidence score. Vector Generalization Vector generalization is a technique used in the YOLO algorithm to handle the high dimensionality of the output. The output of the YOLO algorithm is a tensor that contains the bounding box coordinates, objectness score, and class probabilities. This high-dimensional tensor is flattened into a vector to make it easier to process. The vector is then passed through a softmax function to convert the class scores into probabilities. The final output is a vector that contains the bounding box coordinates, objectness score, and class probabilities for each grid cell. Evolution of YOLO If you are not interested in a quick recap of the timeline of YOLO models and the updates in the network architecture, skip this section! YOLOv1 The original YOLO model treated object detection as a regression problem, which was a significant shift from the traditional classification approach. It used a single convolutional neural network (CNN) to detect objects in images by dividing the image into a grid, making multiple predictions per grid cell, filtering out low-confidence predictions, and then removing overlapping boxes to produce the final output. YOLOv2 [YOLO9000] YOLOv2 introduced several improvements over the original YOLO. It used batch normalization in all its convolutional layers, which reduced overfitting and improved model stability and performance. It could handle higher-resolution images, making it better at spotting smaller objects. YOLOv2 also used anchor boxes (borrowed from Faster R-CNN), which helped the algorithm predict the shape and size of objects more accurately. YOLOv3 YOLOv3 introduced a new backbone network, Darknet-53, which utilized residual connections. It also made several design changes to improve accuracy while maintaining speed. At 320x320 resolution, YOLOv3 ran in 22 ms at 28.2 mAP, as accurate as SSD but three times faster. It achieved 57.9 mAP@50 in 51 ms on a Titan X, compared to 57.5 mAP@50 in 198 ms by RetinaNet, with similar performance but 3.8x faster. YOLOv4 YOLOv4 introduced several new techniques to improve both accuracy and speed. It used a CSPDarknet backbone and introduced new techniques such as spatial attention, Mish activation function, and GIoU loss to improve accuracy3. The improved YOLOv4 algorithm showed a 0.5% increase in average precision (AP) compared to the original algorithm while reducing the model’s weight file size by 45.3 M. YOLOR Unlike previous YOLO versions, YOLOR’s architecture and model infrastructure differ significantly. The name “YOLOR” emphasizes its unique approach: it combines explicit and implicit knowledge to create a unified network capable of handling multiple tasks with a single input. By learning just one representation, YOLOR achieves impressive performance in object detection. YOLOX YOLOX is an anchor-free object detection model that builds upon the foundation of YOLOv3 SPP with a Darknet53 backbone. It aims to surpass the performance of previous YOLO versions. The key innovation lies in its decoupled head and SimOTA approach. By eliminating anchor boxes, YOLOX simplifies the design while achieving better accuracy. It bridges the gap between research and industry, offering a powerful solution for real-time object detection. YOLOX comes in various sizes, from the lightweight YOLOX-Nano to the robust YOLOX-x, each tailored for different use cases. YOLOv5 YOLOv5 brought about further enhancements to increase both precision and efficiency. It adopted a Scaled-YOLOv4 backbone and incorporated new strategies such as CIOU loss and CSPDarknet53-PANet-SPP to boost precision. Structure of YOLOv5 The refined YOLOv5 algorithm demonstrated a 0.7% rise in mean average precision (mAP) compared to the YOLOv4, while decreasing the model’s weight file size by 53.7 M. These improvements made YOLOv5 a more effective and efficient tool for real-time object detection. YOLOv6 YOLOv6 utilized a CSPDarknet-X backbone and introduced new methods such as panoptic segmentation, Swish activation function, and DIoU loss to boost accuracy. Framework of YOLOv6 The enhanced YOLOv6 algorithm exhibited a 0.8% increase in average precision (AP) compared to the YOLOv5, while shrinking the model’s weight file size by 60.2 M. These advancements made YOLOv6 an even more powerful tool for real-time object detection. YOLOv7 YOLOv7 employed a CSPDarknet-Z backbone and introduced innovative techniques such as object-centric segmentation, Leaky ReLU activation function, and TIoU loss to enhance accuracy. The advanced YOLOv7 algorithm demonstrated a 1.0% increase in average precision (AP) compared to the YOLOv6, while reducing the model’s weight file size by 70.5 M. These improvements made YOLOv7 an even more robust tool for real-time object detection. YOLOv8 YOLOv8 introduced a new backbone architecture, the CSPDarknet-AA, which is an advanced version of the CSPDarknet series, known for its efficiency and performance in object detection tasks. One key technique introduced in YOLOv8 is multi-scale object detection. This technique allows the model to detect objects of various sizes in an image. Another significant enhancement in YOLOv8 is the use of the ELU activation function. ELU, or Exponential Linear Unit, helps to speed up learning in deep neural networks by mitigating the vanishing gradient problem, leading to faster convergence. YOLOv8 adopted the GIoU loss. GIoU, or Generalized Intersection over Union, is a more advanced version of the IoU (Intersection over Union) metric that takes into account the shape and size of the bounding boxes, improving the precision of object localization. The YOLOv8 algorithm shows a 1.2% increase in average precision (AP) compared to the YOLOv7, which is a significant improvement. It has achieved this while reducing the model’s weight file size by 80.6 M, making the model more efficient and easier to deploy in resource-constrained environments. YOLOv8 Comparison with Latest YOLO models YOLOv9  YOLOv9 which was recently released overcame information loss challenges inherent in deep neural networks. By integrating PGI and the versatile GELAN architecture, YOLOv9 not only enhances the model’s learning capacity but also ensures the retention of crucial information throughout the detection process, thereby achieving exceptional accuracy and performance. Key Highlights of YOLOv9 Information Bottleneck Principle: This principle reveals a fundamental challenge in deep learning: as data passes through successive layers of a network, the potential for information loss increases. YOLOv9 counters this challenge by implementing Programmable Gradient Information (PGI), which aids in preserving essential data across the network’s depth, ensuring more reliable gradient generation and, consequently, better model convergence and performance. Reversible Functions: A function is deemed reversible if it can be inverted without any loss of information. YOLOv9 incorporates reversible functions within its architecture to mitigate the risk of information degradation, especially in deeper layers, ensuring the preservation of critical data for object detection tasks. For more information, read the blog YOLOv9: SOTA Object Detection Model Explained.   Object Detection with Pre-Trained YOLOv9 on COCO Like all YOLO models, the pre-trained models of YOLOv9 is open-source and is available in GitHub. We are going to run our experiment on Google Colab. So if you are doing it on your local system, please bear in mind that the instructions and the code was made to run on Colab Notebook. Make sure you have access to GPU. You can either run the command below or navigate to Edit → Notebook settings → Hardware accelerator, set it to GPU, and the click Save. !nvidia-smi To make it easier to manage datasets, images, and models we create a HOME constant. import os HOME = os.getcwd() print(HOME) Clone and Install !git clone https://github.com/SkalskiP/yolov9.git %cd yolov9 !pip install -r requirements.txt -q Download Model Weights !wget -P {HOME}/weights -q https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-c.pt !wget -P {HOME}/weights -q https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-e.pt !wget -P {HOME}/weights -q https://github.com/WongKinYiu/yolov9/releases/download/v0.1/gelan-c.pt !wget -P {HOME}/weights -q https://github.com/WongKinYiu/yolov9/releases/download/v0.1/gelan-e.pt Test Data Upload test image to the Colab notebook. !wget -P {HOME}/data -q –-add image path Detection with Pre-trained COCO Model on gelan-c !python detect.py --weights {HOME}/weights/gelan-c.pt --conf 0.1 --source image path --device 0 Evaluation of the Pre-trained COCO Model on gelan-c !python val.py --data data/coco.yaml --img 640 --batch 32 --conf 0.001 --iou 0.7 --device 0 --weights './gelan-c.pt' --save-json --name gelan_c_640_val Performance of YOLOv9 on MS COCO Dataset Yolov9: Learning What You Want to Learn Using Programmable Gradient Information  The performance of YOLOv9 on the MS COCO dataset exemplifies its significant advancements in real-time object detection, setting new benchmarks across various model sizes. The smallest of the models, v9-S, achieved 46.8% AP on the validation set of the MS COCO dataset, while the largest model, v9-E, achieved 55.6% AP. This sets a new state-of-the-art for object detection performance. These results demonstrate the effectiveness of YOLOv9’s techniques, such as Programmable Gradient Information (PGI) and the Generalized Efficient Layer Aggregation Network (GELAN), in enhancing the model’s learning capacity and ensuring the retention of crucial information throughout the detection process. For more information, read the paper of Arxiv: Yolov9: Learning What You Want to Learn Using Programmable Gradient Information.   Training YOLOv9 on Custom Dataset Here for training data, we will be curating a custom dataset on Encord platform. With Encord you can either curate and create your custom dataset or use the sandbox datasets already created on Encord Active platform. Select New Dataset to Upload Data You can name the dataset and add a description to provide information about the dataset. Annotate Custom Dataset Create an annotation project and attach the dataset and the ontology to the project to start annotation with a workflow. You can choose manual annotation if the dataset is simple, small, and doesn’t require a review process. Automated annotation is also available and is very helpful in speeding up the annotation process. For more information on automated annotation, read the blog The Full Guide to Automated Data Annotation.   Start Labeling The summary page shows the progress of the annotation project. The information regarding the annotators and the performance of the annotators can be found under the tabs labels and performance. Export the Annotation Once the annotation has been reviewed, export the annotation in the required format. For more information on exploring the quality of your custom dataset, read the blog Exploring the Quality of Hugging Face Image Datasets with Encord Active.   You can use the custom dataset curated using Encord Annotate for training an object detection model. For testing YOLOv9, we are going to use an image from one of the sandbox projects on Encord Active. Copy and run the code below to run YOLOv9 for object detection. The code for using YOLOv9 for panoptic segmentation has also been made available now on the original GitHub repository. Installing YOLOv9 !git clone https://github.com/SkalskiP/yolov9.git   %cd yolov9 !pip install -r requirements.txt -q !pip install -q roboflow encord av # This is a convenience class that holds the info about Encord projects and makes everything easier. # The class supports bounding boxes and polygons across both images, image groups, and videos. !wget 'https://gist.githubusercontent.com/frederik-encord/e3e469d4062a24589fcab4b816b0d6ec/raw/fa0bfb0f1c47db3497d281bd90dd2b8b471230d9/encord_to_roboflow_v1.py' -O encord_to_roboflow_v1.py Imports from typing import Literal from pathlib import Path from IPython.display import Image import roboflow from encord import EncordUserClient from encord_to_roboflow_v1 import ProjectConverter Download YOLOv9 Model Weights The YOLOv9 is available as 4 models which are ordered by parameter count: YOLOv9-S YOLOv9-M YOLOv9-C YOLOv9-E Here we will be using gelan-c. But the same process follows for other models. !mkdir -p {HOME}/weights !wget -q https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-e-converted.pt -O {HOME}/weights/yolov9-e.pt !wget -P {HOME}/weights -q https://github.com/WongKinYiu/yolov9/releases/download/v0.1/gelan-c.pt Train Custom YOLOv9 Model for Object Detection !python train.py \ --batch 8 --epochs 20 --img 640 --device 0 --min-items 0 --close-mosaic 15 \ --data $dataset_yaml_file \ --weights {HOME}/weights/gelan-c.pt \ --cfg models/detect/gelan-c.yaml \ --hyp hyp.scratch-high.yaml For more information on end-to-end training YOLOv9 with custom dataset, check out the blog Comparative Analysis of YOLOv9 and YOLOv8 Using Custom Dataset on Encord Active. Object Detection using YOLOv9 on Custom Dataset In order to perform object detection, you have to run prediction of the trained YOLOv9 on custom dataset. Run Prediction import torch augment = False visualize = False conf_threshold = 0.25 nms_iou_thres = 0.45 max_det = 1000 seen, windows, dt = 0, [], (Profile(), Profile(), Profile()) for path, im, im0s, vid_cap, s in dataset: with dt[0]: im = torch.from_numpy(im).to(model.device).float() im /= 255 # 0 - 255 to 0.0 - 1.0 if len(im.shape) == 3: im = im[None] # expand for batch dim # Inference with dt[1]: pred = model(im, augment=augment, visualize=visualize)[0] # NMS with dt[2]: filtered_pred = non_max_suppression(pred, conf_threshold, nms_iou_thres, None, False, max_det=max_det) print(pred, filtered_pred) break Generate YOLOv9 Prediction on Custom Data import matplotlib.pyplot as plt from matplotlib.patches import Rectangle from PIL import Image img = Image.open(Image path) fig, ax = plt.subplots() ax.imshow(img) ax.axis("off") for p, c in zip(filtered_pred[0], ["r", "b", "g", "cyan"]): x, y, w, h, score, cls = p.detach().cpu().numpy().tolist() ax.add_patch(Rectangle((x, y), w, h, color="r", alpha=0.2)) ax.text(x+w/2, y+h/2, model.names[int(cls)], ha="center", va="center", color=c) fig.savefig("/content/predictions.jpg") YOLOv9 Vs YOLOv8: Comparative Analysis Using Encord You can convert the model predictions and upload them to Encord. Here for example, the YOLOv9 and YOLOv8 have been trained and compared on the Encord platform using the xView3 dataset, which contains aerial imagery with annotations for maritime object detection. The comparative analysis between YOLOv9 and YOLOv8 on the Encord platform focuses on precision, recall, and metric analysis. These metrics are crucial for evaluating the performance of object detection models. Precision: Precision measures the proportion of true positives (i.e., correct detections) among all detections. A higher precision indicates fewer false positives. Recall: Recall measures the proportion of actual positives that are correctly identified. A higher recall indicates fewer false negatives. Metric Analysis: This involves analyzing various metrics like Average Precision (AP), Mean Average Precision (mAP), etc., which provide a comprehensive view of the model’s performance. For example, in the precision-recall curve, it seems that YOLOv8 surpasses YOLOv9 in terms of the Area Under the Curve (AUC-PR) value. This suggests that, across various threshold values, YOLOv8 typically outperforms YOLOv9 in both precision and recall. It implies that YOLOv8 is more effective at correctly identifying true positives and reducing false positives compared to YOLOv9. But it is important to keep in mind that these two models which are being evaluated were trained for 20 epochs and are used as an example to show how to perform evaluation of trained models on custom datasets. For detailed information on performing a comparative analysis of trained models, read the blog Comparative Analysis of YOLOv9 and YOLOv8 Using Custom Dataset on Encord Active. YOLO Real-Time Implementation YOLO (You Only Look Once) models are widely used in real-time object detection tasks due to their speed and accuracy. Here are some real-world applications of YOLO models: Healthcare: YOLO models can be used in healthcare for tasks such as identifying diseases or abnormalities in medical images. Agriculture: YOLO models have been used to detect and classify crops, pests, and diseases, assisting in precision agriculture techniques and automating farming processes. Security Surveillance: YOLO models are used in security surveillance systems for real-time object detection, tracking, and classification. Self-Driving Cars: In autonomous vehicles, YOLO models are used for detecting objects such as other vehicles, pedestrians, traffic signs, and signals in real-time. Face Detection: They have also been adapted for face detection tasks in biometrics, security, and facial recognition systems YOLO Object Detection: Key Takeaways In this article, we provided an overview of the evolution of YOLO, from YOLOv1 to YOLOv8, and discussed its network architecture, new features, and applications. Additionally, we provided a step-by-step guide on how to use YOLOv8 for object detection and how to create model-assisted annotations with Encord Annotate. At Encord, we help computer vision companies build better models and training datasets. We have built an end-to-end Active Learning Platform for AI-assisted annotation workflows evaluating and evaluating your training data Orchestrating active learning pipelines Fixing data and label errors Diagnosing model errors & biases.  Encord integrates the new YOLOv8 state-of-the-art model and allows you to train Micro-models on a backbone of YOLOv8 models to support your AI-assisted annotation work. 

March 22

7 min

sampleImage_dimentionality-reduction-techniques-machine-learning
Top 12 Dimensionality Reduction Techniques for Machine Learning

Dimensionality reduction is a fundamental technique in machine learning (ML) that simplifies datasets by reducing the number of input variables or features. This simplification is crucial for enhancing computational efficiency and model performance, especially as datasets grow in size and complexity. High-dimensional datasets, often comprising hundreds or thousands of features, introduce the "curse of dimensionality." This effect slows down algorithms by making data scarceness (sparsity) and computing needs grow exponentially.  Dimensionality reduction changes the data into a simpler, lower-dimensional space that is easier to work with while keeping its main features. This makes computation easier and lowers the risk of overfitting.   This strategy is increasingly indispensable in the era of big data, where managing vast volumes of information is a common challenge. This article provides insight into various approaches, from classical methods like principal component analysis (PCA) and linear discriminant analysis (LDA) to advanced techniques such as manifold learning and autoencoders.  Each technique has benefits and works best with certain data types and ML problems. This shows how flexible and different dimensionality reduction methods are for getting accurate and efficient model performance when dealing with high-dimensional data. Here are the Twelve (12) techniques you will learn in this article: Manifold Learning (t-SNE, UMAP) Principal Component Analysis (PCA) Independent Component Analysis (ICA) Sequential Non-negative Matrix Factorization (NMF) Linear Discriminant Analysis (LDA) Generalized Discriminant Analysis (GDA) Missing Values Ratio (MVR): Threshold Setting Low Variance Filter High Correlation Filter Forward Feature Construction Backward Feature Elimination Autoencoders Classification of Dimensionality Reduction Techniques Dimensionality reduction techniques preserve important data, make it easier to use in other situations, and speed up learning. They do this using two steps: feature selection, which preserves the most important variables, and feature projection, which creates new variables by combining the original ones in a big way. Feature Selection Techniques Techniques classified under this category can identify and retain the most relevant features for model training. This approach helps reduce complexity and improve interpretability without significantly compromising accuracy. They are divided into: Embedded Methods: These integrate feature selection within model training, such as LASSO (L1) regularization, which reduces feature count by applying penalties to model parameters and feature importance scores from Random Forests. Filters: These use statistical measures to select features independently of machine learning models, including low-variance filters and correlation-based selection methods. More sophisticated filters involve Pearson’s correlation and Chi-Squared tests to assess the relationship between each feature and the target variable. Wrappers: These assess different feature subsets to find the most effective combination, though they are computationally more demanding. Feature Projection Techniques Feature projection transforms the data into a lower-dimensional space, maintaining its essential structures while reducing complexity. Key methods include: Manifold Learning (t-SNE, UMAP). Principal Component Analysis (PCA). Kernel PCA (K-PCA). Linear Discriminant Analysis (LDA). Quadratic Discriminant Analysis (QDA). Generalized Discriminant Analysis (GDA). 1. Manifold Learning Manifold learning, a subset of non-linear dimensionality reduction techniques, is designed to uncover the intricate structure of high-dimensional data by projecting it into a lower-dimensional space. Understanding Manifold Learning At the heart of Manifold Learning is that while data may exist in a high-dimensional space, the intrinsic dimensionality—representing the true degrees of freedom within the data—is often much lower. For example, images of faces, despite being composed of thousands of pixels (high-dimensional data points), might be effectively described with far fewer dimensions, such as the angles and distances between key facial features. Core Techniques and Algorithms t-Distributed Stochastic Neighbor Embedding (t-SNE): t-SNE is powerful for visualizing high-dimensional data in two or three dimensions. It converts similarities between data points to joint probabilities and minimizes the divergence between them in different spaces, excelling in revealing clusters within data. Uniform Manifold Approximation and Projection (UMAP): UMAP is a relatively recent technique that balances the preservation of local and global data structures for superior speed and scalability. It's computationally efficient and has gained popularity for its ability to handle large datasets and complex topologies. Isomap (Isometric Mapping): Isomap extends classical Multidimensional Scaling (MDS) by incorporating geodesic distances among points. It's particularly effective for datasets where the manifold (geometric surface) is roughly isometric to a Euclidean space, allowing global properties to be preserved. Locally Linear Embedding (LLE): LLE reconstructs high-dimensional data points from their nearest neighbors, assuming the manifold is locally linear. By preserving local relationships, LLE can unfold twisted or folded manifolds. t-SNE and UMAP are two of the most commonly applied dimensionality reduction techniques. At Encord, we use UMAP to generate the 2D embedding plots in Encord Active. 2. Principal Component Analysis (PCA) The Principal Component Analysis (PCA) algorithm is a method used to reduce the dimensionality of a dataset while preserving as much information (variance) as possible. As a linear reduction method, PCA transforms a complex dataset with many variables into a simpler one that retains critical trends and patterns. What is variance? Variance measures the data spread around the mean, and features with low variance indicate little variation in their values. These features often need to be more formal for subsequent analysis and can hinder model performance. What is Principal Component Analysis (PCA)? PCA identifies and uses the principal components (directions that maximize variance and are orthogonal to each other) to effectively project data into a lower-dimensional space. This process begins with standardizing the original variables, ensuring their equal contribution to the analysis by normalizing them to have a zero mean and unit variance. Step-by-Step Explanation of Principal Component Analysis Standardization: Normalize the data so each variable contributes equally, addressing PCA's sensitivity to variable scales. Covariance Matrix Computation: Compute the covariance matrix to understand how the variables of the input dataset deviate from the mean and to see if they are related (i.e., correlated). Finding Eigenvectors and Eigenvalues: Find the new axes (eigenvectors) that maximize variance (measured by eigenvalues), making sure they are orthogonal to show that variance can go in different directions. Sorting and Ranking: Prioritize eigenvectors (and thus principal components) by their ability to capture data variance, using eigenvalues as the metric of importance. Feature Vector Formation: Select a subset of eigenvectors based on their ranking to form a feature vector. This subset of eigenvectors forms the principal components. Transformation: Map the original data into this principal component space, enabling analysis or further machine learning in a more tractable, less noisy space. Dimensionality reduction using PCA Applications PCA is widely used in exploratory data analysis and predictive modeling. It is also applied in areas like image compression, genomics for pattern recognition, and financial data for uncovering latent patterns and correlations.  PCA can help visualize complex datasets by reducing data dimensionality. It can also make machine learning algorithms more efficient by reducing computational costs and avoiding overfitting with high-dimensional data. 3. Independent Component Analysis (ICA) Independent Component Analysis (ICA) is a computational method in signal processing that separates a multivariate signal into additive, statistically independent subcomponents. Statistical independence is critical because Gaussian variables maximize entropy given a fixed variance, making non-Gaussianity a key indicator of independence.  Originating from the work of Hérault and Jutten in 1985, ICA excels in applications like the "cocktail party problem," where it isolates distinct audio streams amid noise without prior source information. Example of the cocktail party problem The cocktail party problem involves separating original sounds, such as music and voice, from mixed signals recorded by two microphones. Each microphone captures a different combination of these sounds due to its varying proximity to the sound sources. ICA is distinct from methods like PCA because it focuses on maximizing statistical independence between components rather than merely de-correlating them.   Principles Behind Independent Component Analysis The essence of ICA is its focus on identifying and separating independent non-Gaussian signals embedded within a dataset. It uses the fact that these signals are statistically independent and non-Gaussian to divide the mixed signals into separate parts from different sources.  This demixing process is pivotal, transforming seemingly inextricable data (impossible to separate) into interpretable components. Two main strategies for defining component independence in ICA are the minimization of mutual information and non-Gaussianity maximization. Various algorithms, such as infomax, FastICA, and kernel ICA, implement these strategies through measures like kurtosis and negentropy​​. Algorithmic Process To achieve its goals, ICA incorporates several preprocessing steps: Centering adjusts the data to have a zero mean, ensuring that analyses focus on variance rather than mean differences. Whitening transforms the data into uncorrelated variables, simplifying the subsequent separation process. After these steps, ICA applies iterative methods to separate independent components, and it often uses auxiliary methods like PCA or singular value decomposition (SVD) to lower the number of dimensions at the start. This sets the stage for efficient and robust component extraction. By breaking signals down into basic, understandable parts, ICA provides valuable information and makes advanced data analysis easier, which shows its importance in modern signal processing and beyond. Let’s see some of its applications. Applications of ICA The versatility of ICA is evident across various domains: In telecommunications, it enhances signal clarity amidst interference. Finance benefits from its ability to identify underlying factors in complex market data, assess risk, and detect anomalies. In biomedical signal analysis, it dissects EEG or fMRI data to isolate neurological activity from artifacts (such as eye blinks). 4. Sequential Non-negative Matrix Factorization (NMF) Nonnegative matrix Factorization (NMF) is a technique in multivariate analysis and linear algebra in which a matrix V is factorized into two lower-dimensional matrices, W (basis matrix) and H (coefficient matrix), with the constraint that all matrices involved have no negative elements.  This factorization works especially well for fields where the data is naturally non-negative, like genetic expression data or audio spectrograms, because it makes it easy to understand the parts.  The primary aim of NMF is to reduce dimensionality and uncover hidden/latent structures in the data.   Principle of Sequential Non-negative Matrix Factorization The distinctive aspect of Sequential NMF is its iterative approach to decomposing matrix V into  W and H, making it adept at handling time-series data or datasets where the temporal evolution of components is crucial. This is particularly relevant in dynamic datasets or applications where data evolves. Sequential NMF responds to changes by repeatedly updating W and H, capturing changing patterns or features important in online learning, streaming data, or time-series analysis. In text mining, for example, V denotes a term-document matrix over time, where W represents evolving topics and H indicates their significance across different documents or time points. This dynamic representation allows the monitoring of trends and changes in the dataset's underlying structure. Procedure of feature extraction using NMF Applications The adaptability of Sequential NMF has led to its application in a broad range of fields, including: Medical Research: In oncology, Sequential NMF plays a pivotal role in analyzing genetic data over time, aiding in the classification of cancer types, and identifying temporal patterns in biomarker expression. Audio Signal Processing: It is used to analyze sequences of audio signals and capture the temporal evolution of musical notes or speech. Astronomy and Computer Vision: Sequential NMF tracks and analyzes the temporal changes in celestial bodies or dynamic scenes. 5. Linear Discriminant Analysis (LDA) Linear Discriminant Analysis (LDA) is a supervised machine learning technique used primarily for pattern classification, dimensionality reduction, and feature extraction. It focuses on maximizing class separability.  Unlike PCA, which optimizes for variance regardless of class labels, LDA aims to find a linear combination of features that separates different classes. It projects data onto a lower-dimensional space using class labels to accomplish this. Imagine, for example, a dataset of two distinct groups of points spread in space; LDA aims to find a projection where these groups are as distinct as possible, unlike PCA, which would look for the direction of highest variance regardless of class distinction.   This method is highly efficient in scenarios where the division between categories of data is to be accentuated. PCA Vs. LDA: What's the Difference? Assumptions of LDA Linear Discriminant Analysis (LDA) operates under assumptions essential for effectively classifying observations into predefined groups based on predictor variables. These assumptions, elaborated below, play a critical role in the accuracy and reliability of LDA's predictions. Multivariate Normality: Each class must follow a multivariate normal distribution (multi-dimensional bell curve). You can asses this through visual plots or statistical tests before applying LDA. Homogeneity of Variances (Homoscedasticity): Ensuring uniform variance across groups helps maintain the reliability of LDA's projections. Techniques like Levene's test can assess this assumption. Absence of Multicollinearity: LDA requires predictors to be relatively independent. Techniques like variance inflation factors (VIFs) can diagnose multicollinearity issues. Working Methodology of Linear Discriminant Analysis  LDA transforms the feature space into a lower-dimensional one that maximizes class separability by: Calculating mean vectors for each class. Computing within-class and between-class scatter matrices to understand the distribution and separation of classes. Solving for the eigenvalues and eigenvectors that maximize the between-class variance relative to the within-class variance. This defines the optimal projection space to distinguish the classes. Tools like Python's Scikit-learn library simplify applying LDA with functions specifically designed to carry out these steps efficiently. Applications LDA's ability to reduce dimensionality while preserving as much of the class discriminatory information as possible makes it a powerful feature extraction and classification tool applicable across various domains. Examples: In facial recognition, LDA enhances the distinction between individual faces to improve recognition accuracy. Medical diagnostics benefit from LDA's ability to classify patient data into distinct disease categories, aiding in early and accurate diagnosis. In marketing, LDA helps segment customers for targeted marketing campaigns based on demographic and behavioral data. 6. Generalized Discriminant Analysis (GDA) Generalized Discriminant Analysis (GDA) extends linear discriminant analysis (LDA) into a nonlinear domain. It uses kernel functions to project input data vectors into a higher-dimensional feature space to capture complex patterns that LDA, limited to linear boundaries, might miss.  These functions project data into a higher-dimensional space where inseparable classes in the original space can be distinctly separated. Step-by-step Explanation of Generalized Discriminant Analysis  The core objective of GDA is to find a low-dimensional projection that maximizes the between-class scatter while minimizing the within-class scatter in the high-dimensional feature space. Let’s examine the GDA algorithm step by step: 1. Kernel Function Selection: First, choose an appropriate kernel function (e.g., polynomial, radial basis function (RBF)) that transforms the input data into a higher-dimensional space. 2. Kernel Matrix Computation: Compute the kernel matrix K, representing the high-dimensional dot products between all pairs of data points. This matrix is central to transforming the data into a feature space without explicitly performing the computationally expensive mapping. 3. Scatter Matrix Calculation in Feature Space: In the feature space, compute the within-class scatter matrix SW and the between-class scatter matrix SB, using the kernel matrix K to account for the data's nonlinear transformation. 4. Eigenvalue Problem: Solving this problem in the feature space identifies the projection vectors that best separate the classes by maximizing the SB/SW ratio. This step is crucial for identifying the most informative projections for class separation. 5. Projection: Use the obtained eigenvectors to project the input data onto a lower-dimensional space that maximizes class separability to achieve GDA's goal of improved class recognition. Applications GDA has been applied in various domains, benefiting from its ability to handle nonlinear patterns: Image and Video Recognition: GDA is used for facial recognition, object detection, and activity recognition in videos, where the data often exhibit complex, nonlinear relationships. Biomedical Signal Processing: In analyzing EEG, ECG signals, and other biomedical data, GDA helps distinguish between different physiological states or diagnose diseases. Text Classification and Sentiment Analysis: GDA transforms text data into a higher-dimensional space, effectively separating documents or sentiments that are not linearly separable in the original feature space. 7. Missing Values Ratio (MVR): Threshold Setting Datasets often contain missing values, which can significantly impact the effectiveness of dimensionality reduction techniques. One approach to addressing this challenge is to utilize a missing values ratio (MVR) thresholding technique for feature selection. Process of Setting Threshold for Missing Values  The MVR for a feature is calculated as the percentage of missing values for data points. The optimal threshold is dependent on several factors, including the dataset’s nature and the intended analysis: Determining the Threshold: Use statistical analyses, domain expertise, and exploratory data analysis (e.g., histograms of missing value ratios) to identify a suitable threshold. This decision balances retaining valuable data against excluding features that could introduce bias or noise. Implications of Threshold Settings: A high threshold may retain too many features with missing data, complicating the analysis. Conversely, a low threshold could lead to excessive data loss. Regularly, thresholds between 20% to 60% are considered, but this range varies widely based on the data context and analysis goals. Contextual Considerations: The dataset's specific characteristics and the chosen dimensionality reduction technique influence the threshold setting. Methods sensitive to data sparsity or noise may require a lower MVR threshold. Example: In a dataset with 100 observations, a feature with 75 missing values has an MVR of 75%. If the threshold is set at 70%, this feature would be considered for removal. Applications High-throughput Biological Data Analysis: Technical limitations often render Gene expression data incomplete. Setting a conservative MVR threshold may preserve crucial biological insights by retaining genes with marginally incomplete data. Customer Data Analysis: Customer surveys may have varying completion rates across questions. MVR thresholding identifies which survey items provide the most complete and reliable data, sharpening customer insights. Social Media Analysis: Social media data can be sparse, with certain users' entries missing. MVR thresholding can help select informative features for user profiling or sentiment analysis. 8. Low Variance Filter A low variance filter is a straightforward preprocessing technique aimed at reducing dimensionality by eliminating features with minimal variance, focusing analysis on more informative aspects of the dataset. Steps for Implementing a Low Variance Filter Calculate Variance: For each feature in the dataset, compute the variance. Prioritize scaling or normalizing data to ensure variance is measured on a comparable basis across all features. Set Threshold: Define a threshold for the minimum acceptable variance. This threshold often depends on the specific dataset and analysis objectives but typically ranges from a small percentage of the total variance observed across features. Feature Selection: Exclude features with variances below the threshold. Tools like Python's `pandas` library or R's `caret` package can efficiently automate this process. Applications of Low Variance Filter Across Domains Sensor Data Analysis: Sensor readings might exhibit minimal fluctuation over time, leading to features with low variance. Removing these features can help focus on the sensor data's more dynamic aspects. Image Processing: Images can contain features representing background noise. These features often have low variance and can be eliminated using the low variance filter before image analysis. Text Classification: Text data might contain stop words or punctuation marks that offer minimal information for classification. The low variance filter can help remove such features, improving classification accuracy. 9. High Correlation Filter The high correlation filter is a crucial technique for addressing feature redundancy. Eliminating highly correlated features optimizes datasets for improved model accuracy and efficiency. Steps for Implementing a High Correlation Filter Compute Correlation Matrix: Assess the relationship between all feature pairs using an appropriate correlation coefficient, such as Pearson for continuous features (linear relationships) and Spearman for ordinal (monotonic relationships). Define Threshold: Establish a correlation coefficient threshold above highly correlated features. A common threshold of 0.8 or 0.9 may vary based on specific model requirements and data sensitivity. Feature Selection: Identify sets of features whose correlation exceeds the threshold. From each set, retain only one feature based on criteria like predictive power, data completeness, or domain relevance and remove the others. Applications Financial Data Analysis: Stock prices or other financial metrics might exhibit a high correlation, often reflecting market trends. The high correlation filter can help select a representative subset of features for financial modeling. Bioinformatics: Gene expression data can involve genes with similar functions, leading to high correlation. Selecting a subset of uncorrelated genes can be beneficial for identifying distinct biological processes. Recommendation Systems: User profiles often contain correlated features like similar purchase history or browsing behavior. The high correlation filter can help select representative features to build more efficient recommendation models. While the Low Variance Filter method removes features with minimal variance, discarding data points that likely don't contribute much information, the High Correlation Filter approach identifies and eliminates highly correlated features.  This process is crucial because two highly correlated features carry similar information, increasing redundancy within the model. 10. Forward Feature Construction Forward Feature Construction (FFC) is a methodical approach to feature selection, designed to incrementally build a model by adding features that offer the most significant improvement. This technique is particularly effective when the relationship between features and the target variable is complex and needs to be fully understood. Algorithm for Forward Feature Construction Initiate with a Null Model: Start with a baseline model without any predictors to establish a performance benchmark. Evaluation Potential Additions: For each candidate feature outside the model, assess potential performance improvements by adding that feature.  Select the Best Feature: Incorporate the feature that significantly improves performance. Ensure the model remains interpretable and manageable. Iteration: Continue adding features until further additions fail to offer significant gains, considering computational efficiency and the risk of diminishing returns. Practical Considerations and Implementation Performance Metrics: To gauge improvements, use appropriate metrics, such as the Akaike Information Criterion (AIC) for regression or accuracy and the F1 score for classification, adapting the choice of metric to the model's context. Challenges: Be mindful of computational demands and the potential for multicollinearity. Implementing strategies to mitigate these risks, such as pre-screening features or setting a cap on the number of features, can be crucial. Tools: Leverage software tools and libraries (e.g., R's `stepAIC` or Python's `mlxtend.SequentialFeatureSelector`) that support efficient FFC application and streamline feature selection. Applications of FFC Across Domains Clinical Trials Prediction: In clinical research, FFC facilitates the identification of the most predictive biomarkers or clinical variables from a vast dataset, optimizing models for outcome prediction. Financial Modeling:  In financial market analysis, this method distills a complex set of economic indicators down to a core subset that most accurately forecasts market movements or financial risk. 11. Backward Feature Elimination Backward Feature Elimination (BFE) systematically simplifies machine learning models by iteratively removing the least critical features, starting with a model that includes the entire set of features. This technique is particularly suited for refining linear and logistic regression models, where dimensionality reduction can significantly improve performance and interpretability. Algorithm for Backward Feature Elimination Initialize with Full Model: Construct a model incorporating all available features to establish a comprehensive baseline. Identify and Remove Least Impactful Feature: Determine the feature whose removal least affects or improves the model's predictive performance. Use metrics like p-values or importance scores to eliminate it from the model. Performance Evaluation: After each removal, assess the model to ensure performance remains robust. Utilize cross-validation or similar methods to validate performance objectively. Iterative Optimization: Continue this evaluation and elimination process until further removals degrade model performance, indicating that an optimal feature subset has been reached. Learn how to validate the performance of your ML model in this guide to validation model performance with Encord Active.   Practical Considerations for Implementation Computational Efficiency: Given the potentially high computational load, especially with large feature sets, employ strategies like parallel processing or stepwise evaluation to simplify the Backward Feature Elimination (BFE) process. Complex Feature Interactions: Special attention is needed when features interact or are categorical. Consider their relationships to avoid inadvertently removing significant predictors. Applications Backward Feature Elimination is particularly useful in contexts like: Genomics: In genomics research, BFE helps distill large datasets into a manageable number of significant genes to improve understanding of genetic influences on diseases. High-dimensional Data Analysis: BFE simplifies complex models in various fields, from finance to the social sciences, by identifying and eliminating redundant features. This could reduce overfitting and improve the model's generalizability. While Forward Feature Construction is beneficial for gradually building a model by adding one feature at a time, Backward Feature Elimination is advantageous for models starting with a comprehensive set of features and needing to identify redundancies. 12. Autoencoders Autoencoders are a unique type of neural network used in deep learning, primarily for dimensionality reduction and feature learning. They are designed to encode inputs into a compressed, lower-dimensional form and reconstruct the output as closely as possible to the original input. This process emphasizes the encoder-decoder structure. The encoder reduces the dimensionality, and the decoder attempts to reconstruct the input from this reduced encoding. How Does Autoencoders Work? They achieve dimensionality reduction and feature learning by mimicking the input data through encoding and decoding. 1. Encoding: Imagine a bottle with a narrow neck in the middle. The data (e.g., an image) is the input that goes into the wide top part of the bottle. The encoder acts like this narrow neck, compressing the data into a smaller representation. This compressed version, often called the latent space representation, captures the essential features of the original data. The encoder is typically made up of multiple neural network layers that gradually reduce the dimensionality of the data. The autoencoder learns to discard irrelevant information and focus on the most important characteristics by forcing the data through this bottleneck. 2. Decoding: Now, imagine flipping the bottle upside down. The decoder acts like the wide bottom part, trying to recreate the original data from the compressed representation that came through the neck. The decoder also uses multiple neural network layers, but this time, it gradually increases the data's dimensionality, aiming to reconstruct the original input as accurately as possible. Variants and Advanced Applications Sparse Autoencoders: Introduce regularization terms to enforce sparsity in the latent representation, enhancing feature selection. Denoising Autoencoders: Specifically designed to remove noise from data, these autoencoders learn to recover clean data from noisy inputs, offering superior performance in image and signal processing tasks. Variational Autoencoders (VAEs): VAEs make new data samples possible by treating the latent space as a probabilistic distribution. This opens up new ways to use generative modeling. Training Nuances Autoencoders use optimizers like Adam or stochastic gradient descent (SGD) to improve reconstruction accuracy by improving their weights through backpropagation. Overfitting prevention is integral and can be addressed through methods like dropout, L1/L2 regularization, or a validation set for early stopping. Applications Autoencoders have a wide range of applications, including but not limited to: Dimensionality Reduction: Similar to PCA but more powerful (as non-linear alternatives), autoencoders can perform non-linear dimensionality reductions, making them particularly useful for preprocessing steps in machine learning pipelines. Image Denoising: By learning to map noisy inputs to clean outputs, denoising autoencoders can effectively remove noise from images, surpassing traditional denoising methods in efficiency and accuracy. Generative modeling: Variational autoencoders (VAEs) can make new data samples similar to the original input data by modeling the latent space as a continuous probability distribution.​​ (e.g., Generative Adversarial Networks (GANs)). Impact of Dimensionality Reduction in Smart City Solutions Automotus is a company at the forefront of using AI to revolutionize smart city infrastructure, particularly traffic management.  They achieve this by deploying intelligent traffic monitoring systems that capture vast amounts of video data from urban environments.  However, efficiently processing and analyzing this high-dimensional data presents a significant challenge. This is where dimensionality reduction techniques come into play. The sheer volume of video data generated by Automotus' traffic monitoring systems necessitates dimensionality reduction techniques to make data processing and analysis manageable.  PCA identifies the most significant features in the data (video frames in this case) and transforms them into a lower-dimensional space while retaining the maximum amount of variance. This allows Automotus to extract the essential information from the video data, such as traffic flow patterns, vehicle types, and potential congestion points, without analyzing every pixel. Partnering with Encord, Automotus led to a 20% increase in model accuracy and a 35% reduction in dataset size. This collaboration focused on dimensionality reduction, leveraging Encord Annotate’s flexible ontology, quality control capabilities, and automated labeling features.  That approach helped Automotus reduce infrastructure constraints, improve model performance to provide better data to clients, and reduce labeling costs. Efficiency directly contributes to Automotus's business growth and operational scalability. The team used Encord Active to visually inspect, query, and sort their datasets to remove unwanted and poor-quality data with just a few clicks, leading to a 35% reduction in the size of the datasets for annotation. This enabled the team to cut their labeling costs by over a third. Interested in learning more? Read the full story on Encord's website for more details. Dimensionality Reduction Technique: Key Takeaways Dimensionality reduction techniques simplify models and enhance computational efficiency. They help manage the "curse of dimensionality," improving model generalizability and reducing overfitting risk. These techniques are used for feature selection and extraction, contributing to better model performance. They are applied in various fields, such as image and speech recognition, financial analysis, and bioinformatics, showcasing their versatility. By reducing the number of input variables, these methods ensure models are computationally efficient and capture essential data patterns for more accurate predictions.

March 21

10 min

sampleImage_diffusion-models-with-transformers
Diffusion Transformer (DiT) Models: A Beginner’s Guide

What is a Diffusion Transformer (DiT)? Diffusion Transformer (DiT) is a class of diffusion models that are based on the transformer architecture. Developed by William Peebles at UC Berkeley and Saining Xie at New York University, DiT aims to improve the performance of diffusion models by replacing the commonly used U-Net backbone with a transformer. Introduction to Diffusion Models Diffusion models are a type of generative model that simulates a Markov chain to transition from a simple prior distribution to the data distribution. The process is akin to a particle undergoing Brownian motion, where each step is a small random walk. This is why they are called “diffusion” models.  Diffusion models have been used in various applications such as denoising, super-resolution, and inpainting. One of the key advantages of diffusion models is their ability to generate high-quality samples, which makes them particularly useful in tasks such as image synthesis. Convolutional U-NET Architecture The U-Net architecture is a type of convolutional neural network (CNN) that was developed for biomedical image segmentation. The architecture is designed like a U-shape, hence the name U-Net. It consists of a contracting path (encoder) to capture context and a symmetric expanding path (decoder) for precise localization. The U-Net architecture is unique because it concatenates feature maps from the downsampling path with feature maps from the upsampling path. This allows the network to use information from both the context and localization, enabling it to make more accurate predictions. Vision Transformers Vision Transformers (ViT) are a recent development in the field of computer vision that apply transformer models, originally designed for natural language processing tasks, to image classification tasks. Unlike traditional convolutional neural networks (CNNs) that process images in a hierarchical manner. ViTs treat images as a sequence of patches and capture global dependencies between them. This allows them to model long-range, pixel-level interactions. One of the key advantages of ViTs is their scalability. They can be trained on large datasets and can benefit from larger input image sizes. For more information, read the blog Introduction to Vision Transformers (ViT).   Classifier-free Guidance Classifier-free guidance refers to the approach of guiding a model’s learning process without the use of explicit classifiers. This can be achieved through methods such as self-supervision, where the model learns to predict certain aspects of the data from other aspects, or through reinforcement learning, where the model learns to perform actions that maximize a reward signal. Classifier-free guidance can be particularly useful in situations where labeled data is scarce or expensive to obtain. It allows the model to learn useful representations from the data itself, without the need for explicit labels. Understanding Latent Diffusion Models (LDMs) Latent Diffusion Models (LDMs) are a type of generative model that learn to generate data by modeling it as a diffusion process. This process begins with a simple prior, such as Gaussian noise, and gradually transforms it into the target distribution through a series of small steps. Each step is guided by a neural network, which is trained to reverse the diffusion process. LDMs have been successful in generating high-quality samples in various domains, including images, text, and audio. For more information, read the official paper, High-Resolution Image Synthesis with Latent Diffusion Models.   Convolutional U-NET Backbone: Disadvantages Convolutional U-NETs have been a staple in many computer vision tasks due to their ability to capture local features and maintain spatial resolution. However, they have certain limitations. For one, they often struggle with capturing long-range dependencies and global context in the input data. This is because the receptive field of a convolutional layer is local and finite, and increasing it requires deeper networks and larger filters, which come with their own set of challenges. Moreover, the convolution operation in U-NETs is translation invariant, which means it treats a feature the same regardless of its position in the image. This can be a disadvantage in tasks where the absolute position of features is important. Shifting towards Transformer Backbone Transformers, originally designed for natural language processing tasks, have shown great potential in computer vision tasks. Unlike convolutional networks, transformers can model long-range dependencies without the need for deep networks or large filters. This is because they use self-attention mechanisms, which allow each element in the input to interact with all other elements, regardless of their distance. Moreover, transformers are not translation invariant, which means they can capture the absolute position of features. This is achieved through the use of positional encodings, which add information about the position of each element in the input. Evolution of Latent Patches The concept of latent patches evolved from the need to make transformers computationally feasible for high-resolution images. Applying transformers directly to the raw pixels of high-resolution images is computationally expensive because the complexity of self-attention is quadratic in the number of elements. To overcome this, the image is divided into small patches, and transformers are applied to these patches. This significantly reduces the number of elements and hence the computational complexity. This allows transformers to capture both local features within each patch and global context across patches. Diffusion Transformers (DiT) Vs. Vision Transformers (ViT) While both DiT and ViT use transformers as their backbone and operate on latent patches, they differ in how they generate images and their specific architectural details. Diffusion Transformers (DiT) DiT uses transformers in a latent diffusion process, where a simple prior (like Gaussian noise) is gradually transformed into the target image. This is done by reversing the diffusion process guided by a transformer network. An important aspect of DiT is the concept of diffusion timesteps. These timesteps represent the stages of the diffusion process, and the transformer network is conditioned on the timestep at each stage. This allows the network to generate different features at different stages of the diffusion process. DiT can also be conditioned on ‘class labels’, allowing it to generate images of specific classes. Vision Transformers (ViT) ViT uses transformers to directly generate the image in an autoregressive manner, where each patch is generated one after the other, conditioned on the previously generated patches. A key component of ViT is the use of adaptive layer norm layers (adaLN). These layers adaptively scale and shift the features based on the statistics of the current batch, which helps in stabilizing the training and improving the model’s performance. While both approaches have their strengths and weaknesses, they represent two promising directions for leveraging transformers in generative modeling of images. The choice between DiT and ViT would depend on the specific requirements of the task at hand. For instance, if the task requires generating images of specific classes, DiT might be a better choice due to its ability to condition on class labels. On the other hand, if the task requires generating high-resolution images, ViT might be more suitable due to its use of adaLN layers, which can help in stabilizing the training of large models. Scalable Diffusion Models with Transformers Scalable Diffusion Models with Transformers (DiT) leverage the power of transformers to handle complex tasks involving large-scale data. The scalability of these models allows them to maintain or even improve their performance as the size of the input data increases. This makes them particularly suited for tasks such as natural language processing, image recognition, and other applications where the amount of input data can vary greatly.  Here are some of the features of scalable diffusion models: Gflops - Forward Pass Measurement Gflops, short for gigaflops, is a unit of measurement that quantifies the performance of a computer’s floating-point operations. In the context of machine learning and neural networks, the forward pass measurement in Gflops is crucial as it provides an estimate of the computational resources required for a single forward pass through the network. This measurement is particularly important when dealing with large-scale networks or data, where computational efficiency can significantly impact the feasibility and speed of model training. Lower Gflops indicates a more efficient network in terms of computational resources, which can be a critical factor in resource-constrained environments or real-time applications. Network Complexity vs. Sample Quality The complexity of a neural network is often directly related to the quality of the samples it produces. More complex networks, which may have more layers or more neurons per layer, tend to produce higher quality samples. However, this increased complexity comes at a cost. More complex networks require more computational resources, both in terms of memory and processing power, and they often take longer to train. Conversely, simpler networks are more computationally efficient and faster to train, but they may not capture the nuances of the data as well, leading to lower quality samples. Striking the right balance between network complexity and sample quality is a key challenge in the design of effective neural networks. Variational Autoencoder (VAE)’s Latent Space In a Variational Autoencoder (VAE), the latent space is a lower-dimensional space into which the input data is encoded. This encoding process is a form of dimensionality reduction, where high-dimensional input data is compressed into a lower-dimensional representation. The latent space captures the essential characteristics of the data, and it is from this space that new samples are generated during the decoding process. The quality of the VAE’s output is largely dependent on how well the latent space captures the underlying structure of the input data. If the latent space is too small or not well-structured, the VAE may not be able to generate high-quality samples. If the latent space is well-structured and of appropriate size, the VAE can generate high-quality samples that accurately reflect the characteristics of the input data. Scalability of DiT Scalability is an important feature of Diffusion models with Transformers (DiT). As the size of the input data increases, the model should be able to maintain or improve its performance. This involves efficient use of computational resources and maintaining the quality of the generated samples.  For example, in natural language processing tasks, the size of the input data (i.e., the number of words or sentences) can vary greatly. A scalable DiT model should be able to handle these variations in input size without a significant drop in performance. Furthermore, as the amount of available data continues to grow, the ability of DiT models to scale effectively will become increasingly important. For more information, read the official paper, Scalable Diffusion Models with Transformers.   DiT Scaling Methods There are two primary methods for scaling DiT models: scaling the model size and scaling the number of tokens. Scaling Model Size Scaling the model size involves increasing the complexity of the model, typically by adding more layers or increasing the number of neurons in each layer. This can improve the model’s ability to capture complex patterns in the data, leading to improved performance. However, it also increases the computational resources required to train and run the model. Therefore, it’s important to find a balance between model size and computational efficiency. Scaling Tokens Scaling the number of tokens involves increasing the size of the input data that the model can handle. This is particularly relevant for tasks such as natural language processing, where the input data (i.e., text) can vary greatly in length. By scaling the number of tokens, a DiT model can handle longer texts without a significant drop in performance. However, similar to scaling the model size, scaling the number of tokens also increases the computational resources required, so a balance must be found. Diffusion Transformers Generalized Architecture Spatial Representations The model first inputs spatial representations through a network layer, converting spatial inputs into a sequence of tokens. This process allows the model to handle the spatial information present in the image data. It’s a crucial step as it transforms the input data into a format that the transformer can process effectively. Positional Embeddings Positional embeddings are a critical component of the transformer architecture. They provide the model with information about the position of each token in the sequence. In DiTs, standard Vision Transformer based positional embeddings are applied to all input tokens. This process helps the model understand the relative positions and relationships between different parts of the image. DiT Block Design In a typical diffusion model, a U-Net convolutional neural network (CNN) learns to estimate the noise to be removed from an image. DiTs replace this U-Net with a transformer. This replacement shows that U-Net’s inductive bias is not necessary for the performance of diffusion models. Diffusion Transformer Architecture Variants of DiT blocks handle conditional information with the following blocks: In-context Conditioning In-context conditioning in DiTs involves the use of adaptive layer normalization (adaLN) to inject conditional information into the model. Cross-attention Block The cross-attention in DiTs bridges the interaction between the diffusion network and the image encoder. It mixes two different embedding sequences, allowing the model to capture both local and global information. Conditioning via Adaptive Layer Norm An adaptive layer normalization (adaLN) is used to condition the diffusion network on text representations, enabling parameter-efficient adaptation. Conditioning via Cross-attention Cross-attention is used to bridge the interaction between the diffusion network and the image encoder. It allows attention layers to adapt their behavior at different stages of the denoising process. Conditioning via  Extra Input Tokens While there is limited information available on conditioning via extra input tokens in DiTs, it is known that DiTs with higher Gflops—through increased transformer depth/width or increased number of input tokens—consistently have lower FID. Model Size DiT models range from 33M to 675M parameters and 0.4 to 119 Gflops. They are borrowed from the ViT literature which found that jointly scaling-up depth and width works well. Transformer Decoder The transformer decoder is an architectural upgrade that replaces U-Net with vision transformers (ViT), showing U-Net inductive bias is not necessary for the performance of diffusion models. Training and Inference During training, a diffusion model takes an image to which noise has been added, a descriptive embedding, and an embedding of the current time step. The system learns to use descriptive embedding to remove the noise in successive time steps. At inference, it generates an image by starting with pure noise and a descriptive embedding and removing noise iteratively according to that embedding. Evaluation Metrics The quality of DiT’s output is evaluated according to Fréchet Interception Distance (FID), which measures how the distribution of a generated version of an image compares to the distribution of the original (lower is better). FID improves depending on the processing budget. On 256-by-256-pixel ImageNet images, a small DiT with 6 gigaflops of compute achieves 68.4 FID, a large DiT with 80.7 gigaflops achieves 23.3 FID, and the largest DiT with 119 gigaflops achieves 9.62 FID. A latent diffusion model that used a U-Net (104 gigaflops) achieves 10.56 FID. DiT-XL/2 Models: Trained Versions The DiT-XL/2 models are a series of generative models released by Meta. These models are trained on the ImageNet dataset, a large visual database designed for use in visual object recognition research. The XL/2 in the name refers to the resolution at which the models are trained, with two versions available: one for 512x512 resolution images and another for 256x256 resolution images. 512x512 Resolution on ImageNet The DiT-XL/2 model trained on ImageNet at a resolution of 512x512 uses classifier-free guidance scales of 6.0. The training process for this model took 3M steps. This high-resolution model is designed to handle complex images with intricate details. 256x256 Resolution on ImageNet The DiT-XL/2 model trained on ImageNet at a resolution of 256x256 uses classifier-free guidance scales of 4.0. The training process for this model took 7M steps. This model is optimized for standard resolution images and is more efficient in terms of computational resources. FID Comparisons of the Two Resolutions The DiT-XL/2 model trained at 256x256 resolution outperforms all prior diffusion models, achieving a state-of-the-art FID-50K of 2.27. This is a significant improvement over the previous best FID-50K of 3.60 achieved by the LDM (256x256) model. In terms of compute efficiency, the DiT-XL/2 model is also superior, requiring only 119 Gflops compared to the LDM-4 model’s 103 Gflops and ADM-U’s 742 Gflops. Scalable Diffusion Models with Transformers. At 512x512 resolution, the DiT-XL/2 model again outperforms all prior diffusion models, improving the previous best FID of 3.85 achieved by ADM-U to 3.04. In terms of compute efficiency, the DiT-XL/2 model requires only 525 Gflops, significantly less than ADM-U’s 2813 Gflops. You can find the DiT-XL/2 models on GitHub and run them on HuggingFace or in a Colab Notebook.   Applications of Diffusion Transformer One of the notable applications of DiT is in image generation. Other applications include text summarizations, chatbots, recommendation engines, language translation, knowledge bases, etc. Let’s look at some notable SOTA models which use diffusion transformer architectures: OpenAI’s SORA Video generation models as world simulators OpenAI’s SORA is an AI model that can create realistic and imaginative scenes from text instructions. SORA is a diffusion model, which generates a video by starting off with one that looks like static noise and gradually transforms it by removing the noise over many steps. It can generate videos up to a minute long while maintaining visual quality and adherence to the user’s prompt. SORA is capable of generating entire videos all at once or extending generated videos to make them longer. For more information, read the blog: OpenAI Releases New Text-to-Video Model, Sora   Stable Diffusion 3 Scaling Rectified Flow Transformers for High-Resolution Image Synthesis Stable Diffusion 3 (SD3) is an advanced text-to-image generation model developed by Stability AI. SD3 combines a diffusion transformer architecture and flow matching. It generates high-quality images from textual descriptions. SD3 outperforms state-of-the-art text-to-image generation systems such as DALL·E 3, Midjourney v6, and Ideogram v1 in typography and prompt adherence, based on human preference evaluations. For more information, read the blog: Stable Diffusion 3: Multimodal Diffusion Transformer Model Explained.   PixArt-ɑ PixArt-α: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Syntheis PixArt-α is a Transformer-based Text-to-Image (T2I) diffusion model. Its image generation quality is competitive with state-of-the-art image generators (e.g., Imagen, SDXL, and even Midjourney), reaching near-commercial application standards. PixArt-α supports high-resolution image synthesis up to 1024px resolution with low training cost6. It excels in image quality, artistry, and semantic control. Diffusion Transformer: Key Takeaways Class of Diffusion Models: Diffusion Transformers (DiT) are a novel class of diffusion models that leverage the transformer architecture. Improved Performance: DiT aims to improve the performance of diffusion models by replacing the commonly used U-Net backbone with a transformer. Impressive Scalability: DiT models have demonstrated impressive scalability properties, with higher Gflops consistently having lower Frechet Inception Distance (FID). Versatile Applications: DiT has been applied in various fields, including text-to-video models like OpenAI’s SORA, text-to-image generation models like Stable Diffusion 3, and Transformer-based Text-to-Image (T2I) diffusion models like PixArt-α.

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What is Robotic Process Automation (RPA)?

Robotic process automation (RPA) promotes data-driven automation and digital transformation in modern industries, or “Industrial Revolution 4.0.” Data-driven automation primarily uses insights from data to program software to improve productivity on various tasks. On the other hand, digital transformation approaches create or modify existing products and services, modify businesses, and improve efficiency, customer experience, and overall competitiveness. Modern industries, such as finance, healthcare, manufacturing, and retail, depend on RPA for many automation processes. It is assumed that RPA will overtake approximately 40% of accounting tasks by 2025, indicating a significant shift within the industry. This prediction indicates industries need to adapt RPA to streamline their workflows. Introduction to Robotic Process Automation  RPA is an automation technology that uses software robots or robotic actors to automate repetitive manual tasks. It implements a rigid set of predefined rules and actions to streamline tasks that don’t require human effort. It even leverages technologies like artificial intelligence (AI), the Internet of Things (IoT), and even robotics to achieve automation with intelligence and efficiency.  RPA, coupled with data-driven AI approaches in the current industries, aims to reduce human workload. A straightforward example of RPA in a banking institution is automating repetitive tasks such as data entry for customer transactions, updating customer records, and transaction validations. These processes are well structured and require clear steps and guidelines. Using RPA for such tasks is appropriate as it streamlines the process, reduces processing time, and minimizes errors.    RPA Workflow Likewise, it can be seamlessly integrated with other technologies like blockchain, cloud computing, AR, VR, etc. This improves their capabilities and enables greater productivity, cost savings, and scalability. The traditional way of automating, which involved heavy coding, macro recording, playback, integrating APIs, etc., was slow, complex, and required intensive programming. RPA, on the other hand, offers a sharp contrast. It addresses those issues for automation to be accessible to the masses with its less-code functionality, shallow learning curve, and adaptability. How Does Robotic Process Automation (RPA) Work? Implementing RPA typically follows a structured, four-step process: Understanding the process requires reading the documentation, observing the process, conducting interviews with stakeholders, and conducting user testing. These will provide a list of requirements that adhere to the task and factors affecting the process. Defining workflow automation requires designing the process according to the specific requirements and the complexity of tasks. Depending on the available tools, this may require using low-code platforms with intuitive drag-and-drop interfaces or more advanced systems incorporating machine learning to process unstructured data like text from emails or documents. Integrating with existing systems or processes ensures that RPA bots have the necessary access to perform tasks by interacting with databases, applications, and other digital platforms. Effective integration enables data flow and task execution within the automated workflow. Workflow monitoring and optimization are essential, as they involve overseeing the execution of RPA bots, tracking performance metrics, and identifying any anomalies or issues that may arise during operation. Proactive monitoring enables timely intervention and optimization, ensuring smooth and reliable automation processes. With these steps, you can effectively implement RPA in your workflow. So far, we have seen how RPA benefits repetitive and mundane tasks with a given set of rules. But there are instances where automation can be more than just defining workflows. Sometimes, RPA must reason and make decisions based on the circumstances or data provided. In the next section, we will explore the different types of RPA that satisfy the previous statement.  Types of Robotic Process Automation (RPA) Let us briefly explore how RPA has evolved from a more traditional rule-based automation system to a more intelligent and dynamic data-driven automation technology. Traditional RPA Traditional RPA is designed to automate structured, rule-based tasks that do not require human judgment or decision-making. This approach utilizes predefined steps and workflows to execute repetitive tasks such as data entry, extraction, form filling, and transaction processing.  Traditional RPA is highly effective in streamlining operations that follow a consistent pattern, reducing manual effort and error rates in tasks like invoice processing and routine data management. Applications and Implications of Traditional RPA Automate Logical and Straightforward Tasks: Traditional RPA is ideal for businesses that automate straightforward, voluminous tasks to increase efficiency and accuracy. For example, automating the invoice data entry process can significantly speed up accounts payable operations. Cognitive RPA Cognitive RPA extends the capabilities of traditional automation by integrating artificial intelligence (AI) and machine learning (ML) technologies. This advanced form of RPA can process structured and unstructured data, enabling it to perform tasks requiring contextual understanding, learning from patterns, and making decisions.  RPA Revolution in the Healthcare Industry During COVID-19 Cognitive RPA applications include natural language processing (NLP) and large language models (LLMs) for interpreting human language, sentiment analysis for gauging customer feedback, and image recognition for analyzing visual data.  Applications and Implications: Managing Complex Processes: Cognitive RPA is adept at handling complex processes such as customer service inquiries and analyzing large volumes of diverse data for insights because it adapts to changes and makes informed decisions.  Context-aware Automation: It is suited for more complex challenges like automated customer support, where it can analyze inquiries, understand context, and provide personalized responses. Attended Automation Attended automation involves human collaboration as it works on the cues given by an operator. It is essentially a virtual assistant aiming to boost an individual’s productivity on repetitive tasks. It is also considered a front-end automation tool. It is quite useful for tasks that require human input and judgment to execute a process. Applications and Implications Human + RPA: It is effective for scheduling appointments, customer service interactions, and data validation, where human expertise complements automated processes.  Front-office Tasks: It is primarily preferred for tasks such as receptions, flight booking, check-in automation, etc.  Unattended Automation Unattended automation provides an end-to-end automated solution with no human involvement. The bots are independent and automate the entire workflow. In this case, the RPA is provided with a sequential and clear step to execute.  This type of automation is suitable for executing long processes and works on dedicated machines. An orchestrator allows you to manage tasks by scheduling the entire workflow. You can trigger, monitor, and track your bots with an orchestrator.  Applications and Implications They are suitable for backend processes. They can handle complex tasks like data processing, orchestrating various virtual machines, high-volume transaction processing, data migration between systems, etc.  Hybrid Automation Hybrid automation combines attended and unattended automation. In this type of RPA, communication happens between both processes. Additionally, it combines human involvement and backend operations.  The “attended bots” receive instructions from the human worker and initiate the process. If the process requires triggering unattended bots, these attended bots can do so. Upon triggering, the unattended bots do what they are best at—providing an end-to-end automated service. Once the task is completed, the unattended bots send the data or output to the attended bot, which notifies the human worker for further human input.  Unattended robots handle tasks like data processing, report generation, etc. that don't require human involvement. On the other hand, attended robots handle tasks that require human attention, like gathering data. Applications and Implications Handling Complex Tasks: Hybrid automation excels in airport security check-in, order/delivery routing, inventory management, candidate screening, and interview scheduling.  Robotic Process Automation (RPA) and Artificial Intelligence (AI) In the previous section, we discussed how powerful Cognitive RPA is and how it can handle complex tasks using tools like neural networks and other ML approaches. RPA and AI are powerful individually, but combined, they can achieve and excel much more. This section will discuss how AI can improve RPA capabilities and functionality. Integrating RPA with Computer Vision Let’s discuss in detail how AI can enhance the automation capabilities of RPA via computer vision (CV).  To begin with, we must understand the complexities associated with an image dataset. Image data contains a lot of details and variability. Variability is one of the biggest concerns as it can portray diverse visual and content characteristics, including differences in size, shape, lighting, etc.  Useful: Struggling with detecting and fixing image quality issues for your applications? Use our open-source toolkit, Encord Active OS, to detect image quality issues in this technical tutorial.   The same object captured from different distances can portray different information. However, the same variability in the image contains rich information that, if leveraged properly, can help us get better information about the data.  Example: Suppose you want to analyze thousands of images containing only cars and trucks for autonomous vehicles. You apply a segmentation mask and label each object with a respective class. You can use AI approaches such as CV to apply segmentation masks and assign labels to achieve this. The segmentation process can also represent cars and trucks with different colors for visualization.   Once the segmentation masks are applied to each image, you can use RPA to automate various tasks. For example: It can automate the task of segregating cars and trucks into folders. It can extract and log individual images into a database or a spreadsheet RPA can trigger actions that initiate other required workflows or notifications based on the extracted data. You can see how versatile and beneficial RPA and AI can become when they are combined. You can use AI to perform complex tasks like image segmentation and annotation. However, RPA can build an automated pipeline based on the segmented and annotated images. Useful Read: What are the most prominent use cases of computer vision in robotics? Learn how machine vision powers eight use cases in robotics and automation from this resource.   Now, let’s find out the additional advantages that RPA offers.  Benefits of Robotic Process Automation (RPA) In this section, we will briefly discuss some of RPA's advantages. This will give you insight and help you make informed decisions about implementing RPA in your workflow and businesses. Below are some of the advantages. Low-code Development You can configure RPA software as it offers a UI drag-and-drop feature to define the automation process. This allows users to correctly, logically, and sequentially place the suitable automation component. It also facilitates rapid prototyping, a shallow learning curve, quicker deployment, and even improves collaboration. Increased Efficiency and Productivity RPA reduces human intervention and friction, allowing organizations to automate tasks consistently. This offers an efficient and streamlined workflow, which increases productivity. For example, automating invoice processing, payroll management, data migration, report generation, etc. Cost Savings through Automation RPA reduces human input and workload costs. This means routine work can be done cheaply, and human input can be used in other important areas. By automating repetitive tasks, RPA can save companies 30 to 50% in processing costs. Compared to manual work and traditional methods, this leads to a positive ROI within one year. Improved Accuracy and Compliance As we configure RPA bots with specific predefined rules, we constrain the bots to do that certain task. RPA can improve accuracy for repetitive tasks with well-defined rules by eliminating human error from fatigue and distractions.  RPA software is easy to learn and deploy, and it offers the additional advantages of scalability and efficiency, economic friendliness, and workload reduction. However, it also has challenges. The following section will delve into some of RPA's challenges.  Challenges of RPA We have seen how RPA benefits our repetitive, tedious, and mundane tasks. However, there can be instances where RPA can fail if the task is not correctly defined. Issues can also arise when working with data, among others. Let us now see four common challenges that RPA usually faces. Complexity of Process Identification When automating workflow, it is essential to understand the process because automating the wrong tasks can be detrimental. Carefully analyzing workflows and selecting well-defined, repetitive processes with clear inputs and outputs is essential for success. Integration with Legacy Systems Many organizations utilize older systems not designed for seamless integration with modern automation tools. This can require technical expertise and adaptation to overcome compatibility issues. Security and Compliance Concerns Integrating RPA introduces new access points and data flows. Robust security measures, including data encryption and access controls, are vital to ensure compliance and safeguard sensitive information. Resistance to Change and Organizational Culture Embracing automation often requires organizational shifts and employee training. Addressing concerns about job displacement, upskilling human workers, and fostering a culture of innovation are key to smooth adoption. These challenges often act as a roadblock that may hinder many workflow processes. But if these challenges are carefully addressed, they can help us break barriers and offer new solutions.  Despite the challenges represented in this section, many industries have never refrained from implementing RPA in their workflow. You will learn some of these in the next section. Use Cases This section will discuss three primary industries that use RPA to streamline operations. The industries mentioned here have one thing in common: supply and demand. Because of this factor, freeing up the human workload and automating repetitive and exhausting processes is essential. Healthcare Healthcare organizations are one of the most demanding places where many things can be automated. Because of the ongoing patient visits, especially in hospitals, attending to patients remains a vital obligation compared to other mundane and repetitive tasks. Some of the areas that can be automated using RPA are: Claims Processing: Automating tasks like eligibility verification, data entry, and claims submission can save time, increase accuracy, and improve reimbursement cycles. Patient Scheduling and Registration: Automating appointment scheduling via the RPA app can reduce administrative burden.  Medical Report Generation: Extracting high-volume data from various sources, such as imaging technologies, and generating standardized reports will reduce doctors' and clinicians' workload for patient care. Fraud Detection and Red-Teaming: Analyzing claim data to identify and flag potential fraudulent activity improves healthcare system security and integrity. As patient data requires high security, RPA can also automate various infiltration tests on the healthcare system to check its reliability and security. Retail With the rise of e-commerce and consumer demands, modern retail has enlarged its territory. Here are three ways in which the retail sector is using RPA: Order Processing and Fulfillment: Receiving orders from customers and their delivery is one of the critical jobs of retail. These can be automated using RPA, and customers can be notified regarding each process phase, such as order processing, shipping, etc. This enhances order accuracy and expedites delivery. Customer Service: Chatbots powered by RPA can handle routine inquiries, freeing up human agents for complex issues and improving customer experience. Price Management and Promotions: Automating tasks like price comparisons, discounts based on customer involvement, and campaign execution can promote dynamic pricing strategies and targeted promotions. Supply Chain Management RPA technology has a more significant impact on the supply chain, essentially orchestrating the exchange between various networks. It includes managing and storing raw materials, manufacturing, moving, delivering, and storing finished products in a warehouse.  This is how RPA implementation enhances the supply chain. Purchase Order Processing: RPA automates vendor communication, purchase order generation, and approval cycles, streamlining procurement processes. Improving Supply Chain Planning: RPA can automate data analysis for forecasting and recent trends in markets and products. This eventually promotes better demand planning and inventory management. Logistics and Transportation: Using RPA to automate shipment tracking and route optimization improves logistics efficiency and reduces delays. Case Study: Role of Computer Vision in Enhancing RPA Capabilities in Healthcare In healthcare, a large part is devoted to imaging technology and visual data. For instance, radiology depends on X-rays, CT scans, and other imaging technologies to diagnose and treat patients. Some challenges revolve around this type of data: Image Analysis: Analyzing such images is hard and time-consuming. On average, a radiologist takes about 8 to 10 minutes, sometimes more if the image needs clarification.  Workload Management: Understanding these images takes a lot of time, so it can be exhausting for radiologists to continuously read them and manage other obligations such as attending to the patient and counseling. Additionally, mental exhaustion can cause them to lose focus and make errors in diagnosis and treatment.  Report Generation: This is another phase where radiologists struggle to focus on generating the right and precise patient report through the scan. Overcoming RPA Challenges by Using Computer Vision  Traditional RPA can automate the above challenges with a predefined script but can be inefficient. However, certain tasks like fetching and organizing images can save radiologists time, but they might not be beneficial for complex tasks. This is because the automation script will mostly contain general steps. The software can make errors in anomalies and unclear images and provide the wrong solutions.  For instance, the software may need to analyze the image and correctly interpret the data. Similarly, the software may fail to find anomalies, increase the rate of false positives and negatives, or misclassify the image. If those two cases occur, considerable errors in report generation could lead to the wrong diagnosis and treatment. Computer vision (CV) can be coupled with RPA to address these issues. CV is one of those approaches where you extract rich data representations from visual data. Using CV, RPA can utilize these representations that allow the software to interpret the images and make the right decision. With this combination of AI and RPA, radiologists can quickly receive and review accurate image analysis. This reduces their workload, allowing them to attend to patients or take on complex cases. Additionally, this system can generate reports that the radiologist can review and approve. In a nutshell, systems like this can improve radiologists' accuracy, efficiency, and workload management. Relevant Read: Viz.ai is a San Francisco-based health tech company. Learn how they accelerated the time from diagnosis to treatment using a data-centric CV platform to develop high-quality datasets in this case study. But on the downside, these AI systems need to be trained on a large dataset, which generally takes a lot of time.  What’s Next: Cognitive Automation with Machine Vision?  Cognitive automation has shown great potential, as it can efficiently handle complex tasks. As such, it holds great significance in Machine Vision. A subfield also uses cameras and sensors to get the input data. Modern industrial practices rely on the vision system to manufacture products and services.  Cognitive automation with machine vision can enhance industries to make data-driven decisions, optimize operations, predict challenges, and improve efficiency across various sectors, such as scaling up and down based on requirements, strategic planning, etc. For instance:  Companies developing autonomous vehicles use cameras and sensors to capture environmental data. Cognitive automation processes this data for decision-making, such as updating ML models with anomalies or new insights and integrating them into training simulations. Additionally, it can analyze familiar data, aiding predictive analytics. In the future, cognitive automation may facilitate vehicle-to-vehicle communication, enhancing safety.  In manufacturing, vision systems are pivotal for product analysis and robot navigation. When combined with cognitive automation, new opportunities arise. For instance, it can identify bottlenecks like raw material shortages and automate orders. Furthermore, it can monitor product quality, gather user feedback, and suggest design improvements for future development.  These technologies can promote human-machine collaboration, creating new spaces for innovation and engineering. This can ultimately lead to offering new and better product designs and services and reducing waste.  Robotic Process Automation: Key Takeaways  Robotic Process Automation as automation software and solutions rapidly transforms our work across different fields and processes. With advancements in AI, RPA implementation can be significantly enhanced to boost industrial productivity in a much more innovative way.  As automation technology continues to evolve with RPA, the impact of automation solutions will only grow. They will reshape workflows and open doors for even greater automation possibilities. This will eventually drive research and development in many areas, promoting the betterment of human lives. While challenges exist, its potential for increased efficiency, reduced human error, accuracy, and cost savings is undeniable. Organizations can resolve these challenges by proactively adopting responsible development practices. They can use RPA to navigate the future of work effectively and unlock its full potential for success.

March 15

8 min

sampleImage_open-source-computer-vision-repositories
Top 10 Open Source Computer Vision Repositories

In this article, you will learn about the top 10 open-source Computer Vision repositories on GitHub. We discuss repository formats, their content, key learnings, and proficiency levels the repo caters to. The goal is to guide researchers, practitioners, and enthusiasts interested in exploring the latest advancements in Computer Vision. You will gain insights into the most influential open-source CV repositories to stay up-to-date with cutting-edge technology and potentially incorporate these resources into your projects. Readers can expect a comprehensive overview of the top Computer Vision repositories, including detailed descriptions of their features and functionalities.  The article will also highlight key trends and developments in the field, offering valuable insights for those looking to enhance their knowledge and skills in Computer Vision.  Here’s a list of the repositories we’re going to discuss: Awesome Computer Vision Segment Anything Model (SAM) Visual Instruction Tuning (LLaVA) LearnOpenCV Papers With Code Microsoft ComputerVision recipes Awesome-Deep-Vision Awesome transformer with ComputerVision CVPR 2023 Papers with Code Face Recognition   What is GitHub? GitHub provides developers with a shared environment in which they can contribute code, collaborate on projects, and monitor changes. It also serves as a repository for open-source projects, allowing easy access to code libraries and resources created by the global developer community.   Factors to Evaluate a Github Repository’s Health Before we list the top repositories for Computer Vision (CV), it is essential to understand how to determine a GitHub repository's health. The list below highlights a few factors you should consider to assess a repository’s reliability and sustainability: Level of Activity: Assess the frequency of updates by checking the number of commits, issues resolved, and pull requests. Contribution: Check the number of developers contributing to the repository. A large number of contributors signifies diverse community support. Documentation: Determine documentation quality by checking the availability of detailed readme files, support documents, tutorials, and links to relevant external research papers. New Releases: Examine the frequency of new releases. A higher frequency indicates continuous development. Responsiveness: Review how often the repository authors respond to issues raised by users. High responsiveness implies that the authors actively monitor the repository to identify and fix problems.  Stars Received: Stars on GitHub indicate a repository's popularity and credibility within the developer community. Active contributors often attract more stars, showcasing their value and impact. Top 10 GitHub Repositories for Computer Vision (CV) Open source repositories play a crucial role in CV by providing a platform for researchers and developers to collaborate, share, and improve upon existing algorithms and models.  These repositories host codebases, datasets, and documentation, making them valuable resources for enthusiasts, developers, engineers, and researchers. Let us delve into the top 10 repositories available on GitHub for use in Computer Vision. Disclaimer: Some of the numbers below may have changed after we published this blog post. Check the repository links to get a sense of the most recent numbers.   #1 Awesome Computer Vision The awesome-php project inspired the Awesome Computer Vision repository, which aims to provide a carefully curated list of significant content related to open-source Computer Vision tools.  Awesome Computer Vision Repository Repository Format You can expect to find resources on image recognition, object detection, semantic segmentation, and feature extraction. It also includes materials related to specific Computer Vision applications like facial recognition, autonomous vehicles, and medical image analysis. Repository Contents The repository is organized into various sections, each focusing on a specific aspect of Computer Vision.  Books and Courses: Classic Computer Vision textbooks and courses covering foundational principles on object recognition, computational photography, convex optimization, statistical learning, and visual recognition. Research Papers and Conferences: This section covers research from conferences published by CVPapers, SIGGRAPH Papers, NIPS papers, and survey papers from Visionbib. Tools: It includes annotation tools such as LabelME and specialized libraries for feature detection, semantic segmentation, contour detection, nearest-neighbor search, image captioning, and visual tracking. Datasets: PASCAL VOC dataset, Ground Truth Stixel dataset, MPI-Sintel Optical Flow dataset, HOLLYWOOD2 Dataset, UCF Sports Action Data Set, Image Deblurring, etc. Pre-trained Models: CV models used to build applications involving license plate detection, fire, face, and mask detectors, among others. Blogs: OpenCV, Learn OpenCV, Tombone's Computer Vision Blog, Computer Vision for Dummies, Andrej Karpathy’s blog, Computer Vision Basics with Python Keras, and OpenCV. Key Learnings Visual Computing: Use the repo to understand the core techniques and applications of visual computing across various industries. Convex Optimization: Grasp this critical mathematical framework to enhance your algorithmic efficiency and accuracy in CV tasks. Simultaneous Localization and Mapping (SLAM): Explore the integration of SLAM in robotics and AR/VR to map and interact with dynamic environments. Single-view Spatial Understanding: Learn about deriving 3D insights from 2D imagery to advance AR and spatial analysis applications. Efficient Data Searching: Leverage nearest neighbor search for enhanced image categorization and pattern recognition performance. Aerial Image Analysis: Apply segmentation techniques to aerial imagery for detailed environmental and urban assessment. Proficiency Level Aimed at individuals with an intermediate to advanced understanding of Computer Vision. Commits: 206 | Stars: 19.8k | Forks: 4.1k | Author: Jia-Bin Huang | Repository Link. #2 SegmentAnything Model (SAM) segment-anything is maintained by Meta AI. The Segment Anything Model (SAM) is designed to produce high-quality object masks from input prompts such as points or boxes. Trained on an extensive dataset of 11 million images and 1.1 billion masks, SAM exhibits strong zero-shot performance on various segmentation tasks.  segment-anything repository Repository Format The ReadMe.md file clearly mentions guides for installing these and running the model from prompts. Running SAM from this repo requires Python 3.8 or higher, PyTorch 1.7 or higher, and TorchVision 0.8 or higher. Repository Content The segment-anything repository provides code, links, datasets, etc. for running inference with the SegmentAnything Model (SAM). Here’s a concise summary of the content in the segment-anything repository: This repository provides: Code for running inference with SAM. Links to download trained model checkpoints. Downloadable dataset of images and masks used to train the model. Example notebooks demonstrating SAM usage. Lightweight mask decoder is exportable to the ONNX format for specialized environments. Key Learnings Some of the key learnings one can gain from the segment-anything repository are: Understanding Object Segmentation: Learn about object segmentation techniques and how to generate high-quality masks for objects in images. Explore using input prompts (such as points or boxes) to guide mask generation. Practical Usage of SAM: Install and use Segment Anything Model (SAM) for zero-shot segmentation tasks. Explore provided example notebooks to apply SAM to real-world images. Advanced Techniques: For more experienced users, explore exporting SAM’s lightweight mask decoder to ONNX format for specialized environments. Learn how to fine-tune the Segment Anything Model (SAM) through our comprehensive guide.   Proficiency Level The Segment Anything Model (SAM) is accessible to users with intermediate to advanced Python, PyTorch, and TorchVision proficiency. Here’s a concise breakdown for users of different proficiency levels: Beginner | Install and Run: If you’re new to SAM, follow installation instructions, download a model checkpoint, and use the provided code snippets to generate masks from input prompts or entire images. Intermediate | Explore Notebooks: Dive into example notebooks to understand advanced usage, experiment with prompts, and explore SAM’s capabilities. Advanced | ONNX Export: For advanced users, consider exporting SAM’s lightweight mask decoder to ONNX format for specialized environments supporting ONNX runtime. Commits: 46 | Stars: 42.4k | Forks: 5k | Author: Meta AI Research | Repository Link. #3 Visual Instruction Tuning (LLaVA) Repository The LLaVA (Large Language and Vision Assistant) repository, developed by Haotian Liu, focuses on Visual Instruction Tuning. It aims to enhance large language and vision models, reaching capabilities comparable to GPT-4V and beyond.  LLaVA demonstrates impressive multimodal chat abilities, sometimes even exhibiting behaviors similar to multimodal GPT-4 on unseen images and instructions. The project has seen several releases with unique features and applications, including LLaVA-NeXT, LLaVA-Plus, and LLaVA-Interactive. Visual Instruction Tuning (LLaVA)  Repository Format The content in the LLaVA repository is primarily Python-based. The repository contains code, models, and other resources related to Visual Instruction Tuning. The Python files (*.py) are used to implement, train, and evaluate the models. Additionally, there may be other formats, such as Markdown for documentation, JSON for configuration files, and text files for logs or instructions. Repository Content LLaVA is a project focusing on visual instruction tuning for large language and vision models with GPT-4 level capabilities. The repository contains the following: LLaVA-NeXT: The latest release, LLaVA-NeXT (LLaVA-1.6), has additional scaling to LLaVA-1.5 and outperforms Gemini Pro on some benchmarks. It can now process 4x more pixels and perform more tasks/applications. LLaVA-Plus: This version of LLaVA can plug and learn to use skills. LLaVA-Interactive: This release allows for an all-in-one demo for Image Chat, Segmentation, and Generation. LLaVA-1.5: This version of LLaVA achieved state-of-the-art results on 11 benchmarks, with simple modifications to the original LLaVA. Reinforcement Learning from Human Feedback (RLHF): LLaVA has been improved with RLHF to improve fact grounding and reduce hallucination. Key Learnings The LLaVA repository offers valuable insights in the domain of Visual Instruction Tuning. Some key takeaways include: Enhancing Multimodal Models: LLaVA focuses on improving large language and vision models to achieve capabilities comparable to GPT-4V and beyond. Impressive Multimodal Chat Abilities: LLaVA demonstrates remarkable performance, even on unseen images and instructions, showcasing its potential for multimodal tasks. Release Variants: The project has seen several releases, including LLaVA-NeXT, LLaVA-Plus, and LLaVA-Interactive, each introducing unique features and applications. Proficiency Level Catered towards intermediate and advanced levels Computer Vision engineers building vision-language applications. Commits: 446 | Stars: 14k | Forks: 1.5k | Author : Haotian Liu | Repository Link. #4 LearnOpenCV Satya Mallick maintains a repository on GitHub called LearnOpenCV. It contains a collection of C++ and Python codes related to Computer Vision, Deep Learning, and Artificial Intelligence. These codes are examples for articles shared on the LearnOpenCV.com blog. LearnOpenCV Repository Resource Format The resource format of the repository includes code for the articles and blogs. Whether you prefer hands-on coding or reading in-depth explanations, this repository has diverse resources to cater to your learning style. Repository Contents This repo contains code for Computer Vision, deep learning, and AI articles shared in OpenCV’s blogs, LearnOpenCV.com. You can choose the format that best suits your learning style and interests. Here are some popular topics from the LearnOpenCV repository: Face Detection and Recognition: Learn how to detect and recognize faces in images and videos using OpenCV and deep learning techniques. Object Tracking: Explore methods for tracking objects across video frames, such as using the Mean-Shift algorithm or correlation-based tracking. Image Stitching: Discover how to combine multiple images to create panoramic views or mosaics. Camera Calibration: Understand camera calibration techniques to correct lens distortion and obtain accurate measurements from images with OpenCV. Deep Learning Models: Use pre-trained deep learning models for tasks like image classification, object detection, and semantic segmentation. Augmented Reality (AR): Learn to overlay virtual objects onto real-world scenes using techniques such as marker-based AR. These examples provide practical insights into Computer Vision and AI, making them valuable resources for anyone interested in these fields! Key Learnings Apply OpenCV techniques confidently across varied industry contexts. Undertake hands-on projects using OpenCV that solidify your skills and theoretical understanding, preparing you for real-world Computer Vision challenges. Proficiency Level This repo caters to a wide audience: Beginner: Gain your footing in Computer Vision and AI with introductory blogs and simple projects. Intermediate: Elevate your understanding with more complex algorithms and applications. Advanced: Challenge yourself with cutting-edge research implementations and in-depth blog posts. Commits: 2,333 | Stars: 20.1k | Forks: 11.5k | Author: Satya Mallick | Repository Link. #5 Papers with Code Researchers from Meta AI are responsible for maintaining Papers with Code as a community project. No data is shared with any Meta Platforms product. Papers with Code Repository Repository Format The repository provides a wide range of Computer Vision research papers in various formats, such as:  ResNet: A powerful convolutional neural network architecture with 2052 papers with code. Vision Transformer: Leveraging self-attention mechanisms, this model has 1229 papers with code. VGG: The classic VGG architecture boasts 478 papers with code. DenseNet: Known for its dense connectivity, it has 385 papers with code. VGG-16: A variant of VGG, it appears in 352 papers with code. Repository Contents This repository contains Datasets, Research Papers with Codes, Tasks, and all the Computer Vision-related research material on almost every segment and aspect of CV like The contents are segregated in the form of classified lists as follows:  State-of-the-Art Benchmarks: The repository provides access to a whopping 4,443 benchmarks related to Computer Vision. These benchmarks serve as performance standards for various tasks and models. Diverse Tasks: With 1,364 tasks, Papers With Code covers a wide spectrum of Computer Vision challenges. Whether you’re looking for image classification, object tracking, or depth estimation, you'll find it here. Rich Dataset Collection: Explore 2,842 datasets curated for Computer Vision research. These datasets fuel advancements in ML and allow researchers to evaluate their models effectively. Massive Paper Repository: The platform hosts an impressive collection of 42,212 papers with codes. These papers contribute to cutting-edge research in Computer Vision. Key Learnings Here are some key learnings from the Computer Vision on Papers With Code: Semantic Segmentation: This task involves segmenting an image into regions corresponding to different object classes. There are 287 benchmarks and 4,977 papers with codes related to semantic segmentation. Object Detection: Object detection aims to locate and classify objects within an image. The section covers 333 benchmarks and 3,561 papers with code related to this task. Image Classification: Image classification involves assigning a label to an entire image. It features 464 benchmarks and 3,642 papers with code. Representation Learning: This area focuses on learning useful representations from data. There are 15 benchmarks and 3,542 papers with code related to representation learning. Reinforcement Learning (RL): While not specific to Computer Vision, there is 1 benchmark and 3,826 papers with code related to RL. Image Generation: This task involves creating new images. It includes 221 benchmarks and 1,824 papers with code. These insights provide a glimpse into the diverse research landscape within Computer Vision. Researchers can explore the repository to stay updated on the latest advancements and contribute to the field. Proficiency Levels A solid understanding of Computer Vision concepts and familiarity with machine learning and deep learning techniques are essential to make the best use of the Computer Vision section on Papers With Code. Here are the recommended proficiency levels: Intermediate: Proficient in Python, understanding of neural networks, can read research papers, and explore datasets. Advanced: Strong programming skills, deep knowledge, ability to contribute to research, and ability to stay updated. Benchmarks: 4,443 | Tasks: 1,364 | Datasets: 2,842 | Papers with Code: 42,212 #6  Microsoft / ComputerVision-Recipes The Microsoft GitHub organization hosts various open-source projects and samples across various domains. Among the many repositories hosted by Microsoft, the Computer Vision Recipes repository is a valuable resource for developers and enthusiasts interested in using Computer Vision technologies. Microsoft's Repositories Repository Format One key strength of Microsoft’s Computer Vision Recipes repository is its focus on simplicity and usability. The recipes are well-documented and include detailed explanations, code snippets, and sample outputs. Languages: The recipes are a range of programming languages, primarily Python (with some Jupyter Notebook examples), C#, C++, TypeScript, and JavaScript so that developers can use the language of their choice. Operating Systems: Additionally, the recipes are compatible with various operating systems, including Windows, Linux, and macOS. Repository Content Guidelines: The repository includes guidelines and recommendations for implementing Computer Vision solutions effectively.  Code Samples: You’ll find practical code snippets and examples covering a wide range of Computer Vision tasks. Documentation: Detailed explanations, tutorials, and documentation accompany the code samples. Supported Scenarios: - Image Tagging: Assigning relevant tags to images. - Face Recognition: Identifying and verifying faces in images. - OCR (Optical Character Recognition): Extracting text from images. - Video Analytics: Analyzing videos for objects, motion, and events. Highlights| Multi-Object Tracking: Added state-of-the-art support for multi-object tracking based on the FairMOT approach described in the 2020 paper “A Simple Baseline for Multi-Object Tracking." . Key Learnings The Computer Vision Recipes repository from Microsoft offers valuable insights and practical knowledge in computer vision. Here are some key learnings you can expect: Best Practices: The repository provides examples and guidelines for building computer vision systems using best practices. You’ll learn about efficient data preprocessing, model selection, and evaluation techniques. Task-Specific Implementations: This section covers a variety of computer vision tasks, such as image classification, object detection, and image similarity. By studying these implementations, you’ll better understand how to approach real-world vision problems. Deep Learning with PyTorch: The recipes leverage PyTorch, a popular deep learning library. You’ll learn how to create and train neural networks for vision tasks and explore architectures and techniques specific to computer vision. Proficiency Level The Computer Vision Recipes repository caters to a wide range of proficiency levels, from beginners to experienced practitioners. Whether you’re just starting in computer vision or looking to enhance your existing knowledge, this repository provides practical examples and insights that can benefit anyone interested in building robust computer vision systems. Commits: 906 | Stars: 9.3k | Forks: 1.2k | Author: Microsoft | Repository Link. #7 Awesome-Deep-Vision The Awesome Deep Vision repository, curated by Jiwon Kim, Heesoo Myeong, Myungsub Choi, Jung Kwon Lee, and Taeksoo Kim, is a comprehensive collection of deep learning resources designed specifically for Computer Vision.  This repository offers a well-organized collection of research papers, frameworks, tutorials, and other useful materials relating to Computer Vision and deep learning. Awesome-Deep-Vision Repository Repository Format The Awesome Deep Vision repository organizes its resources in a curated list format. The list includes various categories related to Computer Vision and deep learning, such as research papers, courses, books, videos, software, frameworks, applications, tutorials, and blogs. The repository is a valuable resource for anyone interested in advancing their knowledge in this field. Repository Content Here’s a closer look at the content and their sub-sections of the Awesome Deep Vision repository: Papers: This section includes seminal research papers related to Computer Vision. Notable topics covered include: ImageNet Classification: Papers like Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton’s work on image classification using deep convolutional neural networks. Object Detection: Research on real-time object detection, including Faster R-CNN and PVANET. Low-Level Vision: Papers on edge detection, semantic segmentation, and visual attention. Other resources are Computer Vision course lists, books, video lectures, frameworks, applications, tutorials, and insightful blog posts. Key Learnings The Awesome Deep Vision repository offers several valuable learnings for those interested in Computer Vision and deep learning: Stay Updated: The repository provides a curated list of research papers, frameworks, and tutorials. By exploring these resources, you can stay informed about the latest advancements in Computer Vision. Explore Frameworks: Discover various deep learning frameworks and libraries. Understanding their features and capabilities can enhance your ability to work with Computer Vision models. Learn from Research Papers: Dive into research papers related to Computer Vision. These papers often introduce novel techniques, architectures, and approaches. Studying them can broaden your knowledge and inspire your work. Community Collaboration: The repository is a collaborative effort by multiple contributors. Engaging with the community and sharing insights can lead to valuable discussions and learning opportunities. While the repository doesn’t directly provide model implementations, it is a valuable reference point for anyone passionate about advancing their Computer Vision and deep learning skills.  Proficiency Level The proficiency levels that this repository caters to are: Intermediate: Proficiency in Python programming and awareness of deep learning frameworks. Advanced: In-depth knowledge of CV principles, mastery of frameworks, and ability to contribute to the community. Commits : 207 | Stars : 10.8k | Forks : 2.8k | Author : Jiwon Kim | Repository Link. #8 Awesome Transformer with Computer Vision (CV) The Awesome Visual Transformer repository is a curated collection of articles and resources on transformer models in Computer Vision (CV), maintained by dk-liang.  The repository is a valuable resource for anyone interested in the intersection of visual transformers and Computer Vision (CV). Awesome-visual-transformer Repository Repository Format This repository (Awesome Transformer with Computer Vision (CV)) is a collection of research papers about transformers with vision. It contains surveys, arXiv papers, papers with codes on CVPR, and papers on many other subjects related to Computer Vision. It does not contain any coding.  Repository Content This is a valuable resource for anyone interested in transformer models within the context of Computer Vision (CV). Here’s a brief overview of its content: Papers: The repository collects research papers related to visual transformers. Notable papers include: “Transformers in Vision”: A technical blog discussing vision transformers. “Multimodal learning with transformers: A survey”: An IEEE TPAMI paper. ArXiv Papers: The repository includes various arXiv papers, such as: “Understanding Gaussian Attention Bias of Vision Transformers” “TAG: Boosting Text-VQA via Text-aware Visual Question-answer Generation” Transformer for Classification: - Visual Transformer Stand-Alone Self-Attention in Vision Models: Designed for image recognition, by Ramachandran et al. in 2019. - Transformers for Image Recognition at Scale: Dosovitskiy et al. explore transformers for large-scale image recognition in 2021. Other Topics: The repository covers task-aware active learning, robustness against adversarial attacks, and person re-identification using locally aware transformers. Key Learnings Here are some key learnings from the Awesome Visual Transformer repository: Understanding Visual Transformers: The repository provides a comprehensive overview of visual transformers, including their architecture, attention mechanisms, and applications in Computer Vision. You’ll learn how transformers differ from traditional convolutional neural networks (CNNs) and their advantages. Research Papers and Surveys: Explore curated research papers and surveys on visual transformers. These cover topics like self-attention, positional encodings, and transformer-based models for image classification, object detection, and segmentation. Practical Implementations: The repository includes practical implementations of visual transformers. Studying these code examples will give you insights into how to build and fine-tune transformer-based models for specific vision tasks. Proficiency Level Aimed at Computer Vision researchers and engineers with a practical understanding of the foundational concepts of transformers. Commits: 259 | Stars: 3.2k | Forks: 390 | Author: Dingkang Liang | Repository Link. #9 Papers-with-Code: CVPR 2023 Repository The CVPR2024-Papers-with-Code repository, maintained by Amusi, is a comprehensive collection of research papers and associated open-source projects related to Computer Vision. It covers many topics, including machine learning, deep learning, image processing, and specific areas like object detection, image segmentation, and visual tracking. CVPR2024 Papers with Code Repository Repository Format The repository is an extensive collection of research papers and relevant codes organized according to different topics, including machine learning, deep learning, image processing, and specific areas like object detection, image segmentation, and visual tracking.  Repository Content CVPR 2023 Papers: The repository contains a collection of papers presented at the CVPR 2023 conference. This year (2023), the conference received a record 9,155 submissions, a 12% increase over CVPR 2022, and accepted 2,360 papers for a 25.78% acceptance rate. Open-Source Projects: Along with the papers, the repository also includes links to the corresponding open-source projects. Organized by Topics: The papers and projects in the repository are organized by various topics such as Backbone, CLIP, MAE, GAN, OCR, Diffusion Models, Vision Transformer, Vision-Language, Self-supervised Learning, Data Augmentation, Object Detection, Visual Tracking, and numerous other related topics. Past Conferences: The repository also contains links to papers and projects from past CVPR conferences. Key Learnings Here are some key takeaways from the repository: Cutting-Edge Research: The repository provides access to the latest research papers presented at CVPR 2024. Researchers can explore novel techniques, algorithms, and approaches in Computer Vision. Practical Implementations: The associated open-source code allows practitioners to experiment with and implement state-of-the-art methods alongside research papers. This practical aspect bridges the gap between theory and application. Diverse Topics: The repository covers many topics, including machine learning, deep learning, image processing, and specific areas like object detection, image segmentation, and visual tracking. This diversity enables users to delve into various aspects of Computer Vision. In short, the repository is a valuable resource for staying informed about advancements in Computer Vision and gaining theoretical knowledge and practical skills. Proficiency Level While beginners may find the content challenging, readers with a solid foundation in Computer Vision can benefit significantly from this repository's theoretical insights and practical implementations. Commits: 642 | Stars: 15.2k | Forks: 2.4k | Author: Amusi | Repository Link. #10 Face Recognition This  repository on GitHub provides a simple and powerful facial recognition API for Python. It lets you recognize and manipulate faces from Python code or the command line.  Built using dlib’s state-of-the-art face recognition, this library achieves an impressive 99.38% accuracy on the Labeled Faces in the Wild benchmark. Face Recognition Repository Repository Format  The content of the face_recognition repository on GitHub is primarily in Python. It provides a simple and powerful facial recognition API that allows you to recognize and manipulate faces from Python code or the command line. You can use this library to find faces in pictures, identify facial features, and even perform real-time face recognition with other Python libraries.  Repository Content Here’s a concise list of the content within the face_recognition repository: Python Code Files: The repository contains Python code files that implement various facial recognition functionalities. These files include functions for finding faces in pictures, manipulating facial features, and performing face identification. Example Snippets: The repository provides example code snippets demonstrating how to use the library. These snippets cover tasks such as locating faces in images and comparing face encodings. Dependencies: The library relies on the dlib library for its deep learning-based face recognition. To use this library, you need to have Python 3.3+ (or Python 2.7), macOS or Linux, and dlib with Python bindings installed. Key Learnings Some of the key learnings from the face_recognition repository are: Facial Recognition in Python: It provides functions for locating faces in images, manipulating facial features, and identifying individuals. Deep Learning with dlib: You can benefit from the state-of-the-art face recognition model within dlib. Real-World Applications: By exploring the code and examples, you can understand how facial recognition can be applied in real-world scenarios. Applications include security, user authentication, and personalized experiences. Practical Usage: The repository offers practical code snippets that you can integrate into your projects. It’s a valuable resource for anyone interested in using facial data in Python. Proficiency Level Caters to users with a moderate-to-advanced proficiency level in Python. It provides practical tools and examples for facial recognition, making it suitable for those who are comfortable with Python programming and want to explore face-related tasks. Commits: 238 | Stars: 51.3k | Forks: 13.2k | Author: Adam Geitgey | Repository Link. Key Takeaways Open-source Computer Vision tools and resources greatly benefit researchers and developers in the CV field. The contributions from these repositories advance Computer Vision knowledge and capabilities.  Here are the highlights of this article: Benefits of Code, Research Papers, and Applications: Code, research papers, and applications are important sources of knowledge and understanding. Code provides instructions for computers and devices, research papers offer insights and analysis, and applications are practical tools that users interact with. Wide Range of Topics: Computer Vision encompasses various tasks related to understanding and interpreting visual information, including image classification, object detection, facial recognition, and semantic segmentation. It finds applications in image search, self-driving cars, medical diagnosis, and other fields.

March 15

8 min

sampleImage_github-repositories-image-segmentation
15 Interesting Github Repositories for Image Segmentation

A survey of Image segmentation GitHub Repositories shows how the field is rapidly advancing as computing power increases and diverse benchmark datasets emerge to evaluate model performance across various industrial domains.  Additionally, with the advent of Transformer-based architecture and few-shot learning methods, the artificial intelligence (AI) community uses Vision Transformers (ViT) to enhance segmentation accuracy. The techniques involve state-of-the-art (SOTA) algorithms that only need a few labeled data samples for model training. With around 100 million developers contributing to GitHub globally, the platform is popular for exploring some of the most modern segmentation models currently available.  This article explores the exciting world of segmentation by delving into the top 15 GitHub repositories, which showcase different approaches to segmenting complex images.  But first, let’s understand a few things about image segmentation. What is Image Segmentation? Image segmentation is a computer vision (CV) task that involves classifying each pixel in an image. The technique works by clustering similar pixels and assigning them a relevant label. The method can be categorized into:  Semantic segmentation—categorizes unique objects based on pixel similarity. Instance segmentation— distinguishes different instances of the same object category. For example, instance segmentation will recognize multiple individuals in an image as separate entities, labeling each person as “person 1”, “person 2”, “person 3”, etc. Semantic Segmentation (Left) and Instance Segmentation (Right) The primary applications of image segmentation include autonomous driving and medical imaging. In autonomous driving, segmentation allows the model to classify objects on the road. In medical imaging, segmentation enables healthcare professionals to detect anomalies in X-rays, MRIs, and CT scans. Want to know about best practices for image segmentation? Read our Guide to Image Segmentation in Computer Vision: Best Practices.   Factors to Validate Github Repository’s Health Before we list the top repositories for image segmentation, it is essential to understand how to determine a GitHub repository's health. The list below highlights a few factors you should consider to assess a repository’s reliability and sustainability: Level of Activity: Assess the frequency of updates by checking the number of commits, issues resolved, and pull requests. Contribution: Check the number of developers contributing to the repository. A large number of contributors signifies diverse community support. Documentation: Determine documentation quality by checking the availability of detailed readme files, support documents, tutorials, and links to relevant external research papers. New Releases: Examine the frequency of new releases. A higher frequency indicates continuous development. Responsiveness: Review how often the repository authors respond to issues raised by users. High responsiveness implies that the authors actively monitor the repository to identify and fix problems. Stars Received: Stars on GitHub indicate a repository's popularity and credibility within the developer community. Active contributors often attract more stars, showcasing their value and impact.  Top GitHub Repositories for Image Segmentation Due to image segmentation’s ability to perform advanced detection tasks, the AI community offers multiple open-source GitHub repositories comprising the latest algorithms, research papers, and implementation details. The following sections will overview the fifteen most interesting public repositories, describing their resource format and content, topics covered, key learnings, and difficulty level. #1. Awesome Referring Image Segmentation Referring image segmentation involves segmenting objects based on a natural language query. For example, the user can provide a phrase such as “a brown bag” to segment the relevant object within an image containing multiple objects. Referring image segmentation Resource Format The repository is a collection of benchmark datasets, research papers, and their respective code implementations. Repository Contents The repo comprises ten datasets, including ReferIt, Google-Ref, UNC, and UNC+, and 72 SOTA models for different referring image segmentation tasks. Topics Covered Traditional Referring Image Segmentation: In the repo, you will find frameworks or traditional referring image segmentation, such as LISA, for segmentation through large language models (LLMs). Interactive Referring Image Segmentation: Includes the interactive PhraseClick referring image segmentation model. Referring Video Object Segmentation: Consists of 18 models to segment objects within videos. Referring 3D Instance Segmentation: There are two models for referring 3D instance segmentation tasks for segmenting point-cloud data. Key Learnings Different Types of Referring Image Segmentation: Exploring this repo will allow you to understand how referring interactive, 3D instance, and video segmentation differ from traditional referring image segmentation tasks. Code Implementations: The code demonstrations will help you apply different frameworks to real-world scenarios. Proficiency Level The repo is for expert-level users with a robust understanding of image segmentation concepts. Commits: 71 | Stars: 501 | Forks: 54 | Author: Haoran MO | Repository Link.   #2. Transformer-based Visual Segmentation Transformer-based visual segmentation uses the transformer architecture with the self-attention mechanism to segment objects. Transformer-based Visual Segmentation Resource Format The repo contains research papers and code implementations. Resource Contents It has several segmentation frameworks based on convolutional neural networks (CNNs), multi-head and cross-attention architectures, and query-based models. Topics Covered Detection Transformer (DETR): The repository includes models built on the DETR architecture that Meta introduced. Attention Mechanism: Multiple models use the attention mechanism for segmenting objects. Pre-trained Foundation Model Tuning: Covers techniques for tuning pre-trained models. Key Learnings Applications of Transformers in Segmentation: The repo will allow you to explore the latest research on using transformers to segment images in multiple ways. Self-supervised Learning: You will learn how to apply self-supervised learning methods to transformer-based visual segmentation. Proficiency Level This is an expert-level repository requiring an understanding of the transformer architecture. Commits: 13 | Stars: 549 | Forks: 40 | Author: Xiangtai Li | Repository Link. #3. Segment Anything The Segment Anything Model (SAM) is a robust segmentation framework by Meta AI that generates object masks through user prompts. Segment Anything Model Resource Format The repo contains the research paper and an implementation guide. Resource Contents It consists of Jupyter notebooks and scripts with sample code for implementing SAM and has three model checkpoints, each with a different backbone size. It also provides Meta’s own SA-1B dataset for training object segmentation models. Topics Covered How SAM Works: The paper explains how Meta developed the SAM framework. Getting Started Tutorial: The Getting Started guide helps you generate object masks using SAM. Key Learnings How to Use SAM: The repo teaches you how to create segmentation masks with different model checkpoints. Proficiency Level This is a beginner-level repo that teaches you about SAM from scratch. Commits: 46 | Stars: 42.8k | Forks: 5k | Author: Hanzi Mao | Repository Link.   #4. Awesome Segment Anything The Awesome Segment Anything repository is a comprehensive survey of models using SAM as the foundation to segment anything. SAM mapping image features and prompt embeddings set for a segmentation mask Resource Format The repo is a list of papers and code. Resource Content It consists of SAM’s applications, historical development, and research trends. Topics Covered SAM-based Models: The repo explores the research on SAM-based frameworks. Open-source Projects: It also covers open-source models on platforms like HuggingFace and Colab. Key Learnings SAM Applications: Studying the repo will help you learn about use cases where SAM is relevant. Contemporary Segmentation Methods: It introduces the latest segmentation methods based on SAM. Proficiency Level This is an expert-level repo containing advanced research papers on SAM. Commits: 273 | Stars: 513 | Forks: 39 | Author: Chunhui Zhang | Repository Link.   #5. Image Segmentation Keras The repository is a Keras implementation of multiple deep learning image segmentation models. SAM mapping image features and prompt embeddings set for a segmentation mask Resource Format Code implementations of segmentation models. Resource Content The repo consists of implementations for Segnet, FCN, U-Net, Resnet, PSPNet, and VGG-based segmentation models. Topics Covered Colab Examples: The repo demonstrates implementations through a Python interface. Installation: There is an installation guide to run the relevant modules. Key Learnings How to Use Keras: The repo will help you learn how to implement segmentation models in Keras. Fine-tuning and Knowledge Distillation: The repo contains sections that explain how to fine-tune pre-trained models and use knowledge distillation to develop simpler models. Proficiency Level The repo is an intermediate-level resource for those familiar with Python. Commits: 256 | Stars: 2.8k | Forks: 1.2k | Author: Divam Gupta | Repository Link. #6. Image Segmentation The repository is a PyTorch implementation of multiple segmentation models. R2U-Net Resource Format It consists of code and research papers. Resource Content The models covered include U-Net, R2U-Net, Attention U-Net, and Attention R2U-Net. Topics Covered Architectures: The repo explains the models’ architectures and how they work. Evaluation Strategies: It tests the performance of all models using various evaluation metrics. Key Learnings PyTorch: The repo will help you learn about the PyTorch library. U-Net: It will familiarize you with the U-Net model, a popular framework for medical image segmentation. Proficiency Level This is an intermediate-level repo for those familiar with deep neural networks and evaluation methods in machine learning. Commits: 13 | Stars: 2.4k | Forks: 584 | Author: LeeJunHyun | Repository Link. #7. Portrait Segmentation The repository contains implementations of portrait segmentation models for mobile devices. Portrait Segmentation Resource Format The repo contains code and a detailed tutorial. Resource Content It consists of checkpoints, datasets, dependencies, and demo files. Topics Covered Model Architecture: The repo explains the architecture for Mobile-Unet, Deeplab V3+, Prisma-net, Portrait-net, Slim-net, and SINet. Evaluation: It reports the performance results of all the models. Key Learnings Portrait Segmentation Techniques: The repo will teach you about portrait segmentation frameworks. Model Development Workflow: It gives tips and tricks for training and validating models. Proficiency Level This is an expert-level repo. It requires knowledge of Tensorflow, Keras, and OpenCV. Commits: 405 | Stars: 624 | Forks: 135 | Author: Anilsathyan | Repository Link. #8. BCDU-Net The repository implements the Bi-Directional Convolutional LSTM with U-net (BCDU-Net) for medical segmentation tasks, including lung, skin lesions, and retinal blood vessel segmentation. BCDU-Net Architecture Resource Format The repo contains code and an overview of the model. Resource Content It contains links to the research paper, updates, and a list of medical datasets for training. It also provides pre-trained weights for lung, skin lesion, and blood vessel segmentation models. Topics Covered BCDU-Net Architecture: The repo explains the model architecture in detail. Performance Results: It reports the model's performance statistics against other SOTA frameworks. Key Learnings Medical Image Analysis: Exploring the repo will familiarize you with medical image formats and how to detect anomalies using deep learning models. BCDU-Net Development Principles: It explains how the BCDU-net model works based on the U-net architecture. You will also learn about the Bi-directional LSTM component fused with convolutional layers. Proficiency Level This is an intermediate-level repo. It requires knowledge of LSTMs and CNNs. Commits: 166 | Stars: 656 | Forks: 259 | Author: Reza Azad | Repository Link. #9.MedSegDiff The repository demonstrates the use of diffusion techniques for medical image segmentation. Diffusion Technique Resource Format It contains code implementations and a research paper. Resource Contents It overviews the model architecture and contains the brain tumor segmentation dataset. Topics Covered Model Structure: The repo explains the application of the diffusion method to segmentation problems. Examples: It contains examples for training the model on tumor and melanoma datasets. Key Learnings The Diffusion Mechanism: You will learn how the diffusion technique works. Hyperparameter Tuning: The repo demonstrates a few hyper-parameters to fine-tune the model. Proficiency Level This is an intermediate-level repo requiring knowledge of diffusion methods. Commits: 116 | Stars: 868 | Forks: 130 | Author: Junde Wu | Repository Link. #10. U-Net The repository is a Keras-based implementation of the U-Net architecture. U-Net Architecture Resource Format It contains the original training dataset, code, and a brief tutorial. Resource Contents The repo provides the link to the U-Net paper and contains a section that lists the dependencies and results. Topics Covered U-Net Architecture: The research paper in the repo explains how the U-Net model works. Keras: The topic page has a section that gives an overview of the Keras library. Key Learnings Data Augmentation: The primary feature of the U-net model is its use of data augmentation techniques. The repo will help you learn how the framework augments medical data for enhanced training. Proficiency Level This is a beginner-level repo requiring basic knowledge of Python. Commits: 17 | Stars: 4.4k | Forks: 2k | Author: Zhixuhao | Repository Link. #11. SOTA-MedSeg The repository is a detailed record of medical image segmentation challenges and winning models. Medical Imaging Segmentation Methods Resource Format The repo comprises research papers, code, and segmentation challenges based on different anatomical structures. Resource Contents It mentions the winning models for each year from 2018 to 2023 and provides their performance results on multiple segmentation tasks. Topics Covered Medical Image Segmentation: The repo explores models for segmenting brain, head, kidney, and neck tumors. Past Challenges: It lists older medical segmentation challenges. Key Learnings Latest Trends in Medical Image Processing: The repo will help you learn about the latest AI models for segmenting anomalies in multiple anatomical regions. Proficiency Level This is an expert-level repo requiring in-depth medical knowledge. Commits: 70 | Stars: 1.3k | Forks: 185 | Author: JunMa | Repository Link.   #12. UniverSeg The repository introduces the Universal Medical Image Segmentation (UniverSeg) model that requires no fine-tuning for novel segmentation tasks (e.g. new biomedical domain, new image type, new region of interest, etc). UnverSeg Method Resource Format It contains the research paper and code for implementing the model. Resource Contents The research paper provides details of the model architecture and Python code with an example dataset. Topics Covered UniverSeg Development: The repo illustrates the inner workings of the UniverSeg model. Implementation Guidelines: A ‘Getting Started’ section will guide you through the implementation process. Key Learnings Few-shot Learning: The model employs few-shot learning methods for quick adaptation to new tasks. Proficiency Level This is a beginner-level repo requiring basic knowledge of few-shot learning. Commits: 31 | Stars: 441 | Forks: 41 | Author: Jose Javier | Repository Link.   #13. Medical SAM Adapter The repository introduces the Medical SAM Adapter (Med-SA), which fine-tunes the SAM architecture for medical-specific domains. Med-SA Architecture Resource Format The repo contains a research paper, example datasets, and code for implementing Med-SA. Resource Contents The paper explains the architecture in detail, and the datasets relate to melanoma, abdominal, and optic-disc segmentation. Topics Covered Model Architecture: The research paper in the repo covers a detailed explanation of how the model works. News: It shares a list of updates related to the model. Key Learnings Vision Transformers (ViT): The model uses the ViT framework for image adaptation. Interactive Segmentation: You will learn how the model incorporates click prompts for model training. Proficiency Level The repo is an expert-level resource requiring an understanding of transformers. Commits: 95 | Stars: 759 | Forks: 58 | Author: Junde Wu (via Kids with Tokens) | Repository Link. #14. TotalSegmentator The repository introduces TotalSegmentator, a domain-specific medical segmentation model for segmenting CT images. Subtasks with Classes Resource Format The repo provides a short installation guide, code files, and links to the research paper. Resource Contents The topic page lists suitable use cases, advanced settings, training validation details, a Python API, and a table with all the class names. Topics Covered Total Segmentation Development: The paper discusses how the model works. Usage: It explains the sub-tasks the model can perform. Key Learnings Implementation Using Custom Datasets: The repo teaches you how to apply the model to unique medical datasets. nnU-Net: The model uses nnU-Net, a semantic segmentation model that automatically adjusts parameters based on input data. Proficiency Level The repo is an intermediate-level resource requiring an understanding of the U-Net architecture. Commits: 560 | Stars: 1.1k | Forks: 171 | Author: Jakob Wasserthal | Repository Link.   #15. Medical Zoo Pytorch The repository implements a Pytorch-based library for 3D multi-modal medical image segmentation. Implementing Image Segmentation in PyTorch Resource Format It contains the implementation code and research papers for the models featured in the library. Resource Contents The repo lists the implemented architectures and has a Quick Start guide with a demo in Colab. Topics Covered 3D Segmentation Models: The library contains multiple models, including U-Net3D, V-net, U-Net, and MED3D. Image Data-loaders: It consists of data-loaders for fetching standard medical datasets. Key Learnings Brain Segmentation Performance: The research paper compares the performance of implemented architectures on brain sub-region segmentation. This will help you identify the best model for brain segmentation. COVID-19 Segmentation: The library has a custom model for detecting COVID-19 cases. The implementation will help you classify COVID-19 patients through radiography chest images. Proficiency Level This is an expert-level repo requiring knowledge of several 3D segmentation models. Commits: 122 | Stars: 1.6k | Forks: 288 | Author: Adaloglou Nikolas | Repository Link. GitHub Repositories for Image Segmentation: Key Takeaways While object detection and image classification models dominate the CV space, the recent rise in segmentation frameworks signals a new era for AI in various applications.  Below are a few points to remember regarding image segmentation: Medical Segmentation is the most significant use case. Most segmentation models discussed above aim to segment complex medical images to detect anomalies. Few-shot Learning: Few-shot learning methods make it easier for experts to develop models for segmenting novel images. Transformer-based Architectures: The transformer architecture is becoming a popular framework for segmentation tasks due to its simplicity and higher processing speeds than traditional methods.

March 15

10 min

sampleImage_google-deepmind-sima-ai-agent
Google’s Video Gaming Companion: Scalable Instructable Multiworld Agent [SIMA]

What is DeepMind SIMA? SIMA can follow natural language instructions to perform tasks in various video game environments. It can also generalize across games, picking up skills learned in one game and transferring them to different games. How do you train an AI agent to be a generalist? Google DeepMind’s latest AI agent, SIMA, short for Scalable Instructable Multiworld Agent, helps us understand precisely how. Both NVIDIA and DeepMind have been focused on controlling one multi-world agent. The idea is that if you can develop one agent that can generalize across different domains (for example, different video games), it would probably be quite useful in the real world—for piloting a robot, learning from a physical environment, etc. In this article, you will learn about:  What SIMA is and how it interacts with the environment in real-time using a generic human-like interface. Different methods for training an AI agent. SIMA’s training process, including the environments, data, models, and evaluation methods. How SIMA generalizes knowledge across tasks and environments with really impressive zero-shot capabilities. How useful they are as embodied AI agents. DeepMind’s Gaming Legacy: Alpha Go to Scalable Instructable Multiworld Agent (SIMA) DeepMind has consistently been at the forefront of advancing artificial intelligence (AI) through gaming. This tradition dates back to its groundbreaking success with AlphaGo, famous for beating the world’s best Go players. To understand how the team arrived at SIMA, let’s explore the evolution from DeepMind's early work on reinforcement learning in Atari video games to Scalable Instructable Multiworld Agent (SIMA), focusing on… wait for it… Goat Simulator 3, with some of the funniest game actions . ​​The evolution shows how models go from mastering structured board games to navigating complex, rich, interactive 3D simulations and virtual environments. First off… Atari games. Reinforcement Learning on Atari Video Games DeepMind's first attempt at using AI in games was a huge success when applied to Atari games using deep reinforcement learning (RL). The goal was to get the highest scores in several classic games using only pixel data and game scores. These games provided a diverse platform for testing and improving RL algorithms, which learn optimal behaviors through trial and error, guided by rewards. In this situation, DeepMind's algorithms (the popular AlphaGo, MuZero, and AlphaGo Zero) could master several Atari games, often doing better than humans. This work showed how RL can solve difficult, dynamic, and visually varied problems. It also set a new standard in AI by showing how AI agents can learn and adapt to new environments without having much pre-programmed information. DeepMind's deep Q-network (DQN) was key to this success. It combined deep neural networks with a Q-learning framework to process high-dimensional sensory input and learn successful strategies directly from raw pixels. This approach enabled AI to understand and interact meaningfully with the gaming environment, paving the way for more sophisticated AI applications in gaming and beyond. Scalable Instructable Multiworld Agent (SIMA) on Goat Simulator 3 SIMA builds on its predecessors. The AI agent can move around and interact in a wide range of 3D virtual worlds, not just the 2D worlds of Atari games.  SIMA is built to understand and follow natural language instructions within these environments. This is a first step toward creating general AI that can understand the world and its complexities. SIMA learned from different gaming environments, and one interesting one is Goat Simulator 3. If you have played this game before, you will surely know how unpredictable and chaotic the actions are. It is uniquely challenging due to its open-ended gameplay and humorous, physics-defying mechanics. This, of course, is different from the structured world of Go and other Atari games! To teach SIMA how to operate in Goat Simulator 3, the researchers had to collect a lot of human gameplay from which it could learn. The gameplay included simple navigation to follow specific actions in open-ended language instructions (e.g., “jump the fence”).  This process checks the agent's ability to understand and follow directions and adapt to an environment where nothing is ever the same. Agent Training Methods DeepMind's technical report discusses new ways to train AI agents that use the complexity of simulated environments to help them learn and adapt. These methods are crucial for creating agents like those in the SIMA project that can interact intelligently with various 3D environments. AI Agent Simulator-based Training The method uses reinforcement learning—agents learn the best way to execute a task by trying things out and seeing what works best, with help from reward signals in their environment. In this context, the game environment serves as both the playground and the teacher. Here are the components of this training approach: Reinforcement Learning: The core of this method is an algorithm that adjusts the agent's policy based on the rewards it receives for its actions. The agent learns to connect actions with results, which helps it improve its plan to maximize cumulative rewards. Reward Signals: These signals guide the agent's learning process within game environments. They can be explicit, like points scored in a game, or more nuanced, reflecting progress toward a game's objective or successful interaction within the environment. Environment Flexibility: This training method is flexible because you can use in any setting that provides useful feedback. The agent learns by engaging directly with the environment, navigating a maze, solving puzzles, or interacting with dynamic elements. Examples: Using RL in places like Atari games, where the agent learns different strategies for each game, shows how well this method works. This can also be seen when training agents in more complicated situations, like those in Goat Simulator 3, where the AI must adapt to and understand complex situations with nuance. Traditional Simulator-based Agent Training This method involves unsupervised learning, where the agent explores the environment and learns its dynamics without explicit instruction or reinforcement. The goal is for the agent to develop an intuitive understanding of the rules and mechanics governing the environment. The techniques in this approach are: Unsupervised Model: By interacting with the environment without predefined objectives or rewards, the agent builds a model of the world that reflects its inherent rules and structures. This model helps agents predict outcomes and plan actions, even in unfamiliar scenarios. Learn the Rules Intuitively: The agent notices patterns and regularities in its surroundings by observing and interacting with them. This is the same as "learning the rules of the game." This process helps the agent gain a deep, unconscious understanding that shapes how they act and what they choose to do in the future. Less Need for Annotation: One big benefit of this method is that it does not require as much detailed annotation or guidance. The agent learns from experiences, so it does not need large datasets with labels or manual instructions. Example: Scenarios where agents must infer objectives or navigate environments with sparse or delayed feedback. For example, an agent might learn to identify edible vs. poisonous items in a survival game or deduce the mechanics of object interaction within a physics-driven simulation. Scalable Instructable Multiworld Agent (SIMA) Training Process SIMA's training approach includes several key components, detailed as follows: Scaling Instructable Agents Across Many Simulated Worlds Environment SIMA's training leverages diverse 3D environments, ranging from commercial video games to bespoke research simulations. It was important to the researchers that these environments offer a range of challenges and chances to learn so that agents could become more flexible and generalize to various settings and situations.  Key requirements of these environments include: Diversity: Using open-world games and controlled research environments ensures that agents encounter various scenarios, from dynamic, unpredictable game worlds to more structured, task-focused settings. Rich Interactions: The researchers chose the environments because they allowed agents to interact with different objects, characters, and terrain features in many ways, helping them learn a wide range of skills. Realism and Complexity: Some environments have physics and graphics close to reality. This lets agents learn in situations similar to how complicated things are in the real world. 💡Learn more about these environments in the technical report. Two environments that meet these requirements are: Commercial Video Games: The researchers trained the agents on games, including Goat Simulator 3, Hydroneer, No Man’s Sky, Satisfactory, Teardown, Valheim, and Wobbly Life. Research Environments: These are more controlled environments, such as Controled Labs and procedurally-generated rooms with realistic contents (ProcTHOR). SIMA is capable of performing many actions from language-instructed tasks. Data An extensive and varied set of gameplay data from various environments forms the basis of SIMA's training. This dataset includes: Multimodal Inputs: The multimodal data includes visual observations, spoken instructions, and the actions taken by human players that match. This gives agents a lot of information to learn from. Human Gameplay: The dataset ensures that agents learn from nuanced, contextually appropriate behavior by capturing gameplay and interaction sequences from human players. Annotated Instructions: Language instructions are paired with game sequences to give agents clear examples of using natural language to guide them in doing tasks. Agents SIMA agents are designed to interpret language instructions and execute relevant actions within 3D virtual environments. Key aspects of their design include: Language-Driven Generality: Agents are taught to follow instructions that use open-ended language. This lets them change their actions based on verbal cues to complete many tasks.  Human-Like Interaction: The agents work with a standard interface that looks and feels like a person's. It takes in text and images and responds to keyboard and mouse commands like a person would. Pre-trained Models: SIMA uses pre-trained models, like video models, to process textual and visual data. These models were mostly trained using instruction-conditioned behavioral cloning (see this note) and classifier-free guidance. This makes it easier for the agents to understand complicated instructions and their surroundings. 💡Learn how to go from big to intelligent visual data in our expert-led webinar. Instructions Across SIMA Data Evaluation Methods Assessing the performance of SIMA agents involves a variety of evaluation methods tailored to the different environments and tasks: Ground-truth Evaluation: In research environments, clear success criteria are set for each task, so it is easy to judge an agent's performance by whether certain goals are met.  Human Judgments: When the tasks are more open-ended or subjective, human evaluators watch how the agents act and give feedback on how well they can follow directions and reach their goals while acting like humans. Automated Metrics: In some cases, particularly within commercial games, automated metrics such as in-game scores or task completion indicators provide quantitative measures of agent success. Optical Character Recognition (OCR): Applied in commercial video games where task completion might not be as straightforward to assess. OCR is used to detect on-screen text indicating task completion. Action Log-probabilities and Static Visual Input Tests: These are more simplistic methods assessing the agent's ability to predict actions based on held-out data or to respond to static visual inputs with correct actions. 💡Interested in understanding metrics for computer vision models? Check out our comprehensive article on quality metrics in AI. SIMA Agent Features Scalable Instructable Multiworld Agent (SIMA) incorporates sophisticated features that enable it to interact effectively within various simulated 3D environments. These features are integral to its design, allowing it to understand and execute various natural language instructions and perform many actions across different virtual settings. SIMA agent receives instructions from a user and image observations from the environment Here's a breakdown of these crucial features: Multi-environment Transfer A key feature of SIMA is that it can use the knowledge and skills it has gained in one environment to perform well in another without starting from scratch each time. This ability to transfer between environments is very important for the agent's flexibility and efficiency; it lets it use what it has learned in a wide range of situations instead of just one. For instance, if the agent learns the concept of 'opening a door' in one game, it can apply this knowledge when encountering a door in another unrelated game. The agent's sophisticated perception and action systems facilitate mapping shared concepts by abstracting underlying similarities in interactions across environments and accelerating its adaptation. Understands Natural Language instructions SIMA is engineered to understand a wide range of language instructions, interpreting them within the context of its current environment and objectives. This comprehension extends to complex commands and instruction sequences, enabling SIMA to engage in sophisticated interactions and complete intricate tasks in accordance with human-like language inputs. Performs 600+ Actions Due to the variety of its training environments and the difficulty of the tasks it can handle, SIMA can perform more than 600 different actions. Thanks to its large action repertoire, it can respond correctly to various situations and instructions, which shows how well it has learned to adapt. Average success rate of the SIMA Agent by skill category From basic movements and interactions to more intricate and context-specific actions, SIMA's broad range of capabilities enables it to tackle diverse challenges and objectives. Generalization Rather than mastering a single task or environment, SIMA is developed to generalize its learning and problem-solving capabilities across contexts. This generalization ensures that the agent can apply its learned skills and knowledge to new, unseen challenges, adapting its strategies based on prior experiences and the specific demands of each new setting. Results Highlighting SIMA's Generalization Ability DeepMind's SIMA demonstrates impressive generalization capabilities across various environments, as showcased through several key findings: Zero-Shot Learning Abilities: SIMA effectively applies learned skills to new, unseen environments without additional training, which indicates robust internalized knowledge and skill transferability. No Pre-Training Ablation: Removing pre-trained components affects SIMA's performance, emphasizing the importance of pre-training for generalization. Despite this, some generalization capacity persists, highlighting the robustness of SIMA's core architecture. Language Ablation: Taking out natural language inputs worsens task performance. This shows how important language comprehension is to SIMA's ability to work in diverse environments. Environment-Specialized Performance: SIMA matches or outperforms environment-specialized agents, showcasing its broader applicability and efficient learning across different virtual worlds. Ethical AI Guidelines DeepMind's commitment to ethical AI practices is evident in developing and training SIMA. As part of these ethical guidelines, the AI should only be trained in carefully chosen environments that encourage good values and behavior. Here are the key guidelines they used to avoid violent content: Content Curation: In aligning with ethical AI practices, SIMA's training explicitly avoids video games or environments that feature violent actions or themes. This careful curation ensures that the agent is not exposed to, nor does it learn from, any content that could be considered harmful or contrary to societal norms and values. Promotes Positive Interaction: The training focused on problem-solving, navigation, and constructive interaction, choosing environments without violence. This created an AI agent that can be used in many positive situations. Risk Mitigation: This approach also serves as a risk mitigation strategy, reducing the potential for the AI to develop or replicate aggressive behaviors, which is crucial for maintaining trust and safety in AI deployments. Modeling Safe and Respectful Behaviors: The training program reinforces safe and respectful behaviors and decisions in the agent, ensuring that their actions align with the principles of avoiding harm and promoting well-being. SIMA's training on nonviolent content shows how important it is to ensure that AI research and development align with societal values and that we only create AI that is helpful, safe, and respectful of human rights. Challenges of Developing SIMA The DeepMind SIMA research team faced many difficult problems when developing the agent. These problems arise when training AI agents in different and changing 3D environments, and they show how difficult it is to use AI in situations similar to the complicated and unpredictable real world. Real-time Environments Not Designed for Agents Unpredictable Dynamics: Many real-time environments SIMA is trained in, especially commercial video games, are inherently unpredictable and not specifically designed for AI agents. These environments are crafted for human players and feature nuances and dynamics that can be challenging for AI to navigate and understand. Complex Interactions: The multifaceted interaction possibilities within these environments add another layer of complexity. Agents must learn how to handle various possible events and outcomes, which can change from one moment to the next, just like in real life. Evaluation Without API Access to Environment States Limited Information: Evaluating SIMA's performance without API access means the agent cannot rely on explicit environment states or underlying game mechanics that would typically be available to developers. This limitation necessitates reliance on visual and textual cues alone, which mirrors the human gameplay experience but introduces significant challenges in interpreting and responding to the environment accurately. Assessment Accuracy: The lack of direct environment state access complicates the evaluation process, making it harder to ascertain whether the AI has successfully understood and executed a given task, particularly in complex or ambiguous situations. SIMA’s Current Limitations Although the Scalable Instructable Multiworld Agent (SIMA) has made significant progress, it still has some problems worth mentioning. These constraints highlight areas for future research and development to improve AI agents' capabilities and applications in complex environments. Limited Environmental Availability Diversity of Games: SIMA was trained and tested on four research-based 3D simulations and seven commercial video games. This shows that the model can work in various settings but is still not very broad, considering all the different game types and settings. Adding more types of environments could help test and improve the agent's ability to adapt to new ones. Breadth of 3D Simulations: The four 3D simulations provide controlled settings to test specific agent capabilities. However, increasing the number and diversity of these simulations could offer more nuanced insights into the agent's adaptability and learning efficiency across varied contexts. Restricted Data Pipeline Scalability The current data pipeline, crucial for training SIMA through behavioral cloning, might not be scalable or diverse enough to cover the full spectrum of potential interactions and scenarios an agent could encounter. Improving the scalability and diversity of the data pipeline would be essential for training more robust and versatile AI agents. Short Action Horizon Action Duration: SIMA's training has primarily focused on short-horizon tasks, generally capped at around 10 seconds. This limitation restricts the agent's ability to learn and execute longer and potentially more complex sequences of actions, which are common in real-world scenarios or more intricate game levels. Reliability and Performance Agent Reliability: Although SIMA has shown promise in following instructions and performing actions across various environments, it is often unreliable compared to human performance. The agent's inconsistency in accurately interpreting and executing instructions poses challenges for its deployment in scenarios requiring high precision or critical decision-making. Comparison with Human Performance: Some tasks made for SIMA are naturally hard and require advanced problem-solving and strategic planning, but the agent still does not follow instructions as well as a human would. This shows how hard the environments are and how high the bar was set for the agent since even skilled human players do not get perfect scores on these tasks. Addressing these limitations will be crucial for the next stages of SIMA's development. To make the field of AI agents that can navigate and interact in complex, changing virtual worlds even better, we must improve environmental diversity, data pipeline scalability, action horizon, and overall reliability. Key Takeaways: Google’s Video Gaming Companion—Scalable Instructable Multiworld Agent (SIMA). Here are the key ideas from this article: SIMA interacts with the environment in real-time using a generic human-like interface. It receives image observations and language instructions as inputs and generates keyboard and mouse actions as outputs. SIMA is trained on a dataset of video games, including Satisfactory, No Man's Sky, Goat Simulator 3, and Valheim. The researchers evaluated SIMA’s ability to perform basic skills in these games, such as driving, placing objects, and using tools. On average, SIMA's performance is around 50%, but it is far from perfect. The researchers believe that training AI agents on a broad variety of video games is an effective way to make progress in general AI. These results support SIMA's strong generalization skills and show that it can work well in various situations and tasks. It is a big step forward in developing AI agents with strong, flexible, and transferable skill sets because it shows strong zero-shot learning abilities and resilience against ablation impacts.

March 15

8 min

sampleImage_yolo-world-object-detection
YOLO World Zero-shot Object Detection Model Explained

YOLO-World Zero-shot Real-Time Open-Vocabulary Object Detection is a machine learning model built on the YOLOv8 backbone that excels in identifying a wide array of objects without prior training on specific categories. It achieves high efficiency and real-time performance by integrating vision-language modeling, pre-training on large-scale datasets, and a novel Re-parameterizable Vision-Language Path Aggregation Network (RepVL-PAN).  Object Detection with YOLO Series The YOLO series of detectors initially introduced by Joseph Redmon have revolutionized zero-shot object detection with their real-time performance and straightforward architecture. These detectors operate by dividing the input image into a grid and predicting bounding boxes and class probabilities for each grid cell.  Despite their efficiency, traditional YOLO detectors are trained on datasets with fixed categories, limiting their ability to detect objects beyond these predefined classes without retraining on custom datasets. Read the blog on the latest of the YOLO series: YOLOv9: SOTA Object Detection Model Explained.   Object Detection with Other Vision Language Models Recently, with the introduction of vision foundation models, there has been a surge in research exploring the integration of vision and LLM to enhance object detection capabilities. Models like CLIP (Contrastive Language-Image Pre-training) and F-VLM (Fine-grained Vision-Language Model) have demonstrated the potential of vision-language modeling in various computer vision tasks, including object detection. Grounding DINO Grounding DINO is a method aimed at improving open-set object detection in computer vision. Open-set object detection is a task where models are required to identify and localize objects within images, including those from classes not seen during training, also known as "unknown" or "unseen" object classes. To tackle this challenge, Grounding DINO combines DINO, a self-supervised learning algorithm, with grounded pre-training, which incorporates both visual and textual information. This hybrid approach enhances the model's capability to detect and recognize previously unseen objects in real-world scenarios by leveraging textual descriptions in addition to visual features. For more information, read the paper: Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection.   CLIP CLIP is a neural network trained on a diverse range of images and natural language supervision sourced abundantly from the internet. Unlike traditional models, CLIP can perform various classification tasks instructed in natural language without direct optimization for specific benchmarks. This approach, similar to zero-shot capabilities seen in GPT-2 and GPT-3, enhances the model's robustness and performance, closing the robustness gap by up to 75%. CLIP achieves comparable performance to ResNet-50 on ImageNet zero-shot, without using any of the original labeled examples. For more information, read the paper: Learning Transferable Visual Models From Natural Language Supervision.   F-VLM F-VLM is a simplified open-vocabulary object detection method that leverages Frozen Vision and Language Models (VLM). It eliminates the need for complex multi-stage training pipelines involving knowledge distillation or specialized pretraining for detection. F-VLM demonstrates that a frozen VLM can retain locality-sensitive features crucial for detection and serves as a strong region classifier. The method fine-tunes only the detector head and combines detector and VLM outputs during inference. F-VLM exhibits scaling behavior and achieves a significant improvement of +6.5 mask AP over the previous state-of-the-art on novel categories of the LVIS open-vocabulary detection benchmark.  For more information, read the paper: F-VLM: Open-Vocabulary Object Detection upon Frozen Vision and Language Models.   Open Vocabulary Object Detection in Real-time YOLO-World addresses limitations of traditional object detection methods by enabling open-vocabulary detection beyond fixed categories, offering adaptability to new tasks, reducing computational burden, and simplifying deployment on edge devices. Real-Time Performance YOLO-World retains the real-time performance characteristic of the YOLO architecture. This is crucial for applications where timely detection of objects is required, such as in autonomous vehicles or surveillance systems. Open-Vocabulary Capability YOLO-World has the capability to detect objects beyond the fixed categories which the YOLO series is trained. This open-vocabulary approach allows YOLO-World to identify a broader range of objects making it highly adaptable to diverse real-world scenarios. YOLO-World also presents the "prompt-then-detect" approach, which eliminates the necessity for real-time text encoding. Instead, users can generate prompts, which are subsequently encoded into an offline vocabulary. Integration of Vision-Language Modeling YOLO-World integrates vision-language modeling techniques to enhance its object detection capabilities. By leveraging pre-trained models like CLIP, YOLO-World gains access to semantic information embedded in textual descriptions, which significantly improves its ability to understand and detect objects in images. Efficiency and Practicality Despite its advanced capabilities, YOLO-World remains highly efficient and practical for real-world applications. Its streamlined architecture and efficient implementation ensure that object detection can be performed in real-time without sacrificing accuracy or computational resources. This makes YOLO-World suitable for deployment in a wide range of applications, from robotics to image understanding systems. Open-vocabulary Instance Segmentation Feature In addition to its remarkable object detection capabilities, the pre-trained YOLO-World model also excels in open-vocabulary instance segmentation, demonstrating strong zero-shot performance on large-scale datasets. The open-vocabulary instance segmentation feature of YOLO-World enables it to delineate and segment individual objects within images, regardless of whether they belong to predefined categories or not. By using its comprehensive understanding of visual and textual information, YOLO-World can accurately identify and segment objects based on their contextual descriptions, providing valuable insights into the composition and layout of scenes captured in images. YOLO-World achieves 35.4 Average Precision (AP) on the LVIS dataset while maintaining a high inference speed of 52.0 frames per second (FPS). This underscores the model's ability to accurately segment instances across a wide range of object categories, even without specific prior training on those categories. YOLO-World Framework YOLO-World: Real-Time Open-Vocabulary Object Detection Frozen CLIP-based Text Encoder Frozen CLIP-based Text Encoder, plays a fundamental role in processing textual descriptions associated with objects in images. This text encoder is based on the CLIP (Contrastive Language-Image Pre-training) model, which has been pre-trained on large-scale datasets to understand the relationship between images and corresponding textual descriptions. By leveraging the semantic embeddings generated by the CLIP text encoder, YOLO-World gets access to contextual information about objects, enhancing its ability to interpret visual content accurately. Re-parameterizable Vision-Language Path Aggregation Network The vision-language path aggregation network (RepVL-PAN) serves as the bridge between visual and linguistic information, facilitating the fusion of features extracted from images and textual embeddings derived from the CLIP text encoder. By incorporating cross-modality fusion techniques, RepVL-PAN enhances both the visual and semantic representations of objects. Region-Text Contrastive Loss Region-text contrastive loss involves constructing pairs of regions and their associated textual descriptions, and then calculating the loss using cross-entropy between the predicted object-text similarity and the assigned text indices. YOLO-World incorporates region-text contrastive loss alongside other loss functions such as IoU loss and distributed focal loss for bounding box regression, ensuring comprehensive training and improved performance. This loss function helps YOLO-World learn to accurately associate objects with their corresponding textual descriptions, enhancing the model's object detection capabilities. For more information, read the YOLO-world paper: YOLO-World: Real-Time Open-Vocabulary Object Detection.   YOLO-World Performance Zero-Shot Evaluation on LVIS The YOLO-World model was tested in a zero-shot setting on the Large Vocabulary Instance Segmentation (LVIS) dataset. Despite not being trained in LVIS categories, it performed well, particularly in rare categories. This suggests that the model is effective at generalizing its learned knowledge to new categories. However, it’s important to note that these results are based on internal evaluations and actual performance may vary. YOLO-World: Real-Time Open-Vocabulary Object Detection Speed and Accuracy YOLO-World addresses the limitation of speed in zero-shot object detection models that rely on transformer architectures by applying a faster CNN based YOLO framework. On the challenging LVIS dataset, YOLO-World achieves an impressive 35.4 Average Precision (AP) while maintaining a high inference speed of 52.0 frames per second (FPS) on the V100 platform. This performance surpasses many state-of-the-art methods, highlighting the efficacy of the approach in efficiently detecting a wide range of objects in a zero-shot manner. After fine-tuning, YOLO-World demonstrates remarkable performance across various downstream tasks, including object detection and open-vocabulary instance segmentation, underscoring its versatility and robustness for real-world applications. YOLO-World: Real-Time Open-Vocabulary Object Detection Visualization In visualizations, YOLO-World’s performance is evaluated across three settings: Zero-shot Inference on LVIS: YOLO-World-L detects numerous objects effectively, showcasing its robust transfer capabilities. Inference with User's Vocabulary: YOLO-World-L displays fine-grained detection and classification abilities, distinguishing between sub-categories and even detecting parts of objects. Referring Object Detection: YOLO-World accurately locates regions or objects based on descriptive noun phrases, showcasing its referring or grounding capability. YOLO-World: Real-Time Open-Vocabulary Object Detection Performance Evaluation of YOLO World, GLIP, Grounding DINO In comparing performance on LVIS object detection, YOLO-World demonstrates superiority over recent state-of-the-art methods such as GLIP, GLIPv2, and Grounding DINO in a zero-shot manner. Performance Comparison: GLIP, GLIPv2, and Grounding DINO in a Zero-shot Manner YOLO-World outperforms these methods in terms of both zero-shot performance and inference speed, particularly when considering lighter backbones like Swin-T. Even when compared to models like GLIP, GLIPv2, and Grounding DINO, which utilize additional data sources such as Cap4M, YOLO-World pre-trained on O365 & GolG achieves better performance despite having fewer model parameters.  The Python code for implementing YOLO-World is available on GitHub and you can try out the demo of the object detector on their official site or HuggingFace.   GPU Optimization By efficiently utilizing GPU resources and memory, YOLO-World achieves remarkable speed and accuracy on a single NVIDIA V100 GPU. Leveraging parallel processing capabilities, optimized memory usage, and GPU-accelerated libraries, YOLO-World ensures high-performance execution for both training and inference. YOLO-World Highlights Open-vocabulary detection capability, surpassing fixed category limitations. Efficient adaptation to new tasks without heavy computation burdens. Simplified deployment, making it practical for real-world applications and edge devices. Incorporation of the innovative RepVL-PAN for enhanced performance in object detection. Strong zero-shot performance, achieving significant improvements in accuracy and speed on challenging datasets like LVIS. Easy adaptation to downstream tasks such as instance segmentation and referring object detection. Pre-trained weights and codes made open-source for broader practical use cases. YOLO-World: What’s Next With open-vocabulary object detection, YOLO-World has shown improvement in performance against traditional methods. Moving forward, there are different areas for further research: Efficiency Enhancements: Efforts can be directed towards improving the efficiency of YOLO-World, particularly in terms of inference speed and resource utilization. This involves optimizing model architectures, leveraging hardware acceleration, and exploring novel algorithms for faster computation. Fine-grained Object Detection: YOLO-World could undergo refinement to enhance its capability in detecting fine-grained objects and distinguishing between subtle object categories. This involves exploring advanced feature representation techniques and incorporating higher-resolution image inputs. Semantic Understanding: Future developments could focus on enhancing YOLO-World's semantic understanding capabilities, enabling it to grasp contextual information and relationships between objects within a scene. This involves integrating advanced natural language processing (NLP) techniques and multi-modal fusion strategies. A tutorial on evaluating YOLO World model predictions on Encord is coming up soon!   

March 11

10 min

sampleImage_video-annotation-tool-evaluation
5 Questions to Ask When Evaluating a Video Annotation Tool

With image and video data fueling advancements across various industries, the video and image annotation tool market is witnessing rapid expansion, projected to grow at a compound annual growth rate (CAGR) of 30% between 2023 and 2032. This growth is particularly pronounced in autonomous vehicles, healthcare, and retail sectors, where precise and accurate data annotation is crucial. The increased demand for these tools results from the need to develop robust quality assurance processes, integrate automation for efficiency, collaborate features for team-based annotation, and streamline labeling workflows to produce high-quality training data. However, the extensive choice of annotation tools makes choosing a suitable platform that suits your requirements challenging. There are a plethora of available options, each with varying features, scalability, and pricing models. This article will guide you through this tooling landscape. It highlights five critical questions you must ask before investing in a video annotation tool to ensure it aligns with your project requirements and goals. Key Factors that Hinder Efficient Annotation Project Management A robust video annotation tool helps improve annotation workflows, but selecting an appropriate solution requires you to: Consider the tool’s ability to render videos natively Track objects using advanced algorithms Perform frame-by-frame analysis Doing all those while determining its scalability, quality, integrability, and cost to guide your choice. Below are a few factors that can be potential bottlenecks to your CV project. Native Video Rendering Annotating long-form videos can be challenging if the annotation tool lacks features for rendering videos natively. The operative costs can be prohibitive if you use external tools to render multiple videos, limiting your budget for the annotation project. Object Tracking and Frame-by-Frame Analysis Another obstacle to video annotation is sub-optimal object tracking algorithms that cannot address occlusion, camera shift, and image blur. Traditional tracking algorithms use a detection framework to identify objects within separate video frames. However, detecting and tracking objects frame-by-frame can cause annotation inconsistency and increase data transfer volume. If you are using a cloud platform that charges based on data usage, this will result in inaccurate labels, processing delays, and high storage costs. Scalability Handling large and complex video data is essential for providing a high-quality user experience. However, maintaining quality requires error-free training data with accurate labels to build robust computer vision models that can efficiently process video feeds. Finding a tool that you can quickly scale to rising demands is difficult due to the constantly evolving data landscape. Tools with limited scalability can soon become a bottleneck as you start labeling extensive datasets for training large-scale CV applications. For instance, the pipelines can break as you feed more data. This can result in missed deadlines, deployment delays, and budgetary runs as you hire more annotators to compensate for the tool’s shortcomings. Quality of Annotation Annotation quality directly affects the performance of supervised learning models, which rely heavily on accurately labeled data for training. Consider developing a machine learning model for a surveillance system to detect abnormal behavior and alert relevant authorities to prevent accidents. If the model’s training set included video feeds with erroneous labels, it could not efficiently recognize security threats. This would result in false alarms and missed targets, which would lead to adverse security incidents. Deploying such models in crowded areas can be more detrimental, as the system will not flag suspicious actions in time. Mitigating these problems requires the annotation tool to have quality assurance and collaboration features, which will help human annotators verify labeling accuracy and fix errors proactively. Integrability with Existing Infrastructure Developing robust artificial intelligence (AI) models requires more than the best algorithms and evaluation strategies. Instead, the emphasis should be on an integrated infrastructure that seamlessly handles data collection, storage, preprocessing, and curation. As annotation is a vital element of a data curation pipeline, a tool that quickly integrates with your existing machinery can significantly boost productivity and quality. Businesses that fail to build an integrated system operate multiple disparate systems without synchronization. This results in increased manual effort to organize data assets, which can lead to suboptimal workflows and poor deployment procedures. Cost A data annotation tool that provides flexible pricing options to upgrade or downgrade your plans according to project needs makes financing decisions easier, paving the way for a faster return on investment (ROI). A cost-effective tool helps with executive buy-in as it becomes easier for the management to convince the executive team to undertake innovative projects and continue the development process without budgetary hurdles. Learn how to automate video annotation by reading our guide on video annotation automation. How to Select a Video Annotation Tool Due to the challenges discussed above, choosing a tool that meets your required standards becomes time-consuming and delays the launch of your CV application. So, the following sections explain the primary factors you should consider when investing in a labeling platform. They will help you quickly filter out the desired features to speed up your annotation processes. What are Your Annotation Needs? Understanding the exact annotation requirements should be the first step in selecting a tool, and the following factors must be included: The Type of Computer Vision (CV) Application CV models for applications like autonomous driving and real-time surveillance call for a scalable annotation platform to label large amounts of real-time video feeds. The type of application will also determine what category of annotation is necessary and whether a particular tool offers the required functionality. Critical applications like medical imaging require pixel-level segmentation masks, while bounding boxes will suffice for security surveillance. Automation for Video-specific Complexities Videos with higher frames-per-second (FPS) can take longer to label since annotators must classify objects within each frame. Additionally, videos with higher motion speeds can cause blurred-out frames or motion blur. This is especially true for action recognition CV models, where labeling frequently changing human actions becomes challenging. The solution to these issues is to have tools with automated labeling techniques that use pre-trained models (AI-assisted annotations) to label samples in real time using data pipelines with interpolation algorithms to fix blurry frames. Platform Compatibility and User Interface (UI) A tool compatible with several operating systems and environments can improve integrability and prevent disruptions to annotation projects. Similarly, the tool’s UI must be intuitive so annotators can quickly learn to use the platform, reducing the time required for staff training. Video Format Compatibility For optimal data processing, annotation tools must support multiple video formats, such as MP4, AVI, FLV, etc., and provide features to convert annotations into suitable formats to train CV models quickly. Video Annotation Tool: Must-have Functionalities Based on the above considerations, a video annotation tool must have: Features to natively label video datasets frame-by-frame for advanced object tracking so that minimal downsampling is required. There are basic types of annotations, such as keypoint annotation for pose estimation, 2D bounding boxes, cuboids, polylines, and polygons for labeling objects within a single video frame. Advanced annotation techniques include semantic segmentation, object tracking algorithms, and temporal annotation. Suitable APIs and SDKs can be used to integrate with existing data pipelines programmatically. While these factors are essential for a video annotation tool, it is also advisable to have a manual review process to assess annotation accuracy for high-precision tasks, such as medical imaging, surgical videos, and autonomous navigations. Encord Annotate addresses all the above concerns by offering scalable features and algorithms to handle project complexities, extensive labeling techniques, and automation to speed up the annotation process. How Do You Evaluate Annotation Efficiency? The annotation tool should allow you to compute annotation speed and accuracy through intuitive metrics that reflect actual annotation performance. The list below mentions a few popular metrics for measuring the two factors. Metrics for Measuring Annotation Speed Annotations per hour: Determine the 'annotations per hour' to gauge productivity, contextualizing it with industry norms or project expectations. Frames per minute: Evaluate 'frames per minute' to understand annotator performance in video contexts, considering the video complexity. Time per annotation: Use 'time per annotation' to assess individual annotation task efficiency, adjusting expectations based on the required annotation detail. Metrics for Measuring Annotation Accuracy F1-score: Use the F1-score to balance precision and recall scores, explaining its calculation through Intersection over Union (IoU) in video contexts—IoU determines precision and recall in video frames. Cohen’s Kappa and Fleiss’ Kappa: Use Cohen's Kappa and Fleiss’ Kappa for annotator agreement analysis, providing context for when each is most applicable. Krippendorff’s Alpha: Consider Krippendorff’s alpha for diverse or incomplete datasets, detailing its significance in ensuring consistent annotation quality. Ability to Process Complex Annotation Scenarios Ensure the tool can effectively manage challenges like object occlusion, multiple object tracking, and variable backgrounds. Provide examples to illustrate how these are addressed. Discuss the tool's adaptability to different annotation complexities and how its features facilitate accurate labeling in varied scenarios. Customization and Integrations Customization and integrability with ML models are valuable capabilities that can help you tailor a tool’s annotation features to address use-case-specific needs.  Know if they allow you to use open-source annotation libraries to improve existing functionality. Encord Annotate offers multiple quality metrics to analyze annotation quality and ensures high efficiency that meets current industry standards. How Flexible do you Want the Features to be? While the features mentioned above directly relate to annotation functionality, video annotation software must have other advanced tools to streamline the process for computer vision projects. These include tools for managing ontology, handling long-form video footage, quality control, and AI-based labeling. Ontology Management Ontologies are high-level concepts that specify what and how to label and whether additional information is necessary for model training. Users can define hierarchical structures to relate multiple concepts and create a richer annotated dataset for training CV models. For instance, an ontology for autonomous driving applications specifies that the labeler must annotate a car with 2D bounding boxes and provide information about its model, color, type, etc. These ontologies allow annotators to correctly identify objects of interest in complex videos and include additional information relevant to scene understanding.  Clarifying how users can adapt these ontologies across various project types demonstrates the tool's adaptability to diverse research and industry needs. Features to Manage Long-form Videos Long-form videos pose unique challenges, as annotators must track longer video sequences and manage labels in more frames. Suitable tools that allow you to move back and forth between frames and timelines simplify video analysis. You can easily navigate through the footage to examine objects and scenes. Segmentation: Segmentation is also a valuable feature to look out for, as it allows you to break long videos into smaller segments to create manageable annotation tasks. For instance, automated checks that monitor labels across segments help you identify discrepancies and ensure identical objects have consistent labeling within each segment. Version Control: Finally, version control features let you save and reload previous annotation work, helping you track your progress and synchronize tasks across multiple annotators. Tools that allow annotators to store annotation revision history and tag particular versions help maintain a clear audit trail.  These functionalities improve user experience by reducing fatigue and mitigating errors, as annotators can label long-form videos in separate stages. It also helps with quick recovery in case a particular version becomes corrupt. Customizable Workflows and Performance Monitoring Annotation tools that let you customize workflows and guidelines based on project requirements can improve annotation speed by removing redundancies and building processes that match existing annotators’ expertise. Further, intuitive dashboards that display relevant performance metrics regarding annotation progress and quality can allow management to track issues and make data-driven decisions to boost operational efficiency. Inter-annotator agreement (IAA), annotation speed, and feedback metrics that signify revision cycles are most useful in monitoring annotation efficiency.  For instance, an increasing number of revisions denotes inconsistencies and calls for a root-cause analysis to identify fundamental issues.  AI-assisted Labeling AI-assisted labeling that involves developing models for domain-specific annotation tasks can be costly, as the process requires manual effort to label sufficient samples for pre-training the labeling algorithms. An alternative approach is using techniques like interpolation, semantic and instance segmentation, object tracking, and detection to label video frames without developing a custom model. For example, video annotation tools with object-tracking algorithms can automatically identify objects of interest and fill in the gaps using only a small set of manually labeled data. The method enhances annotation efficiency as annotators do not have to train a separate model from scratch and only label a few items while leaving the rest for AI. Quality Assurance and Access Control Regardless of the level of automation, labeling is error-prone, as it is challenging to annotate each object in all video frames correctly. This limitation requires a tool with quality assurance features, such as feedback cycles, progress trackers, and commenting protocols. These features help human annotators collaborate with experts to identify and fix errors. Efficient access control features also become crucial for managing access across different teams and assigning relevant roles to multiple members within a project. The Encord platform features robust AI-based annotation algorithms, allowing you to integrate custom models, build tailored workflows, and create detailed ontologies to manage long-form videos. What Type of Vendor Are You Looking for? The next vital step in evaluating a tool is assessing different vendors and comparing their annotation services and platforms against standard benchmarks while factoring in upfront and ongoing costs. A straightforward strategy is to list the required features for your annotation project and draw a comparison table to determine which platforms offer these features and at what cost. Here are a few points you should address: Managed Service vs. Standalone Platform: You must see whether you require a managed service or a standalone application. While a managed service frees you from annotating the data in-house, a standalone tool offers more security and transparency in the annotation process. A side-by-side comparison detailing each model's implications on your workflow and data governance practices can guide your decision. Onboarding Costs: Analyze all costs associated with adopting and using the tool, distinguishing between one-time onboarding fees, recurring licensing costs, and any potential hidden fees. Consider creating a multi-year cost projection to understand the total cost of ownership and how it compares to the projected ROI. Ecosystem Strength: A vendor with a robust community and ecosystem offers additional resources to maximize the value of your tool investment, including access to a broader range of insights, support, and potential integrations.  Long-term Suitability: Other relevant factors in evaluating vendors include customer reviews, vendor’s track record in providing regular updates, supporting innovative projects, long-term clients, and customer support quality. Analyzing these will help you assess whether the vendor is a suitable long-run strategic partner who will proactively support your company’s mission and vision.  What is the Standard of Post-purchase Services Investing in a video annotation tool is a long-term strategic action involving repeated interactions with the vendor to ensure a smooth transition process and continuous improvements. Below are a few essential services that vendors must offer post-purchase to provide greater value and meet changing demands as per project requirements. Training Resources: The vendor must provide easy access to relevant training materials, such as detailed documentation, video tutorials, and on-site support, to help users fully utilize the tool’s feature set from the start. Data Security Protocols: While compliance with established security standards, including GDPR, HIPAA, ISO, and SOC, is crucial, the vendor must continuously update its encryption protocols to address the dynamic nature of data and rising privacy concerns. Post-purchase, the vendor must ensure robust security measures by following ethical practices and analyzing sensitive information in your project to implement suitable safeguards to prevent breaches and data misuse. Customer Support: The vendor must offer 24/7 customer support helplines for bug resolution and workflow assistance. Want to know the most crucial features of a video annotation tool? Read our article on the five features of video annotation. Encord complies with HIPAA, FDA, and CE standards, making it an ideal tool for sensitive annotation tasks, especially for medical use cases. Evaluating a Video Annotation Tool: Key Takeaways As CV models permeate multiple domains, such as healthcare, retail, and manufacturing, video annotation tools will be critical determinants of the success of modern CV projects. Below are a few key factors you should consider when evaluating a video annotation platform. Annotation Requirements: The answer will allow you to filter out the desired feature set and scalability demands. Evaluation of Annotation Efficiency: Understanding evaluation methodologies will help you select a tool that offers suitable metrics to assess annotation speed and accuracy. Feature Flexibility: Ontology management, AI-assisted labeling, and options to customize workflows are crucial features that allow you to tailor the tool’s feature set to your requirements. Strategic Vendor Evaluation: Analyzing upfront and ongoing costs helps you determine the total cost of ownership and whether the vendor is a suitable long-term strategic partner. Quality of Post-purchase Services: With the ever-changing data landscape, you need a vendor that constantly updates its security and training protocols to keep pace with ongoing developments.

March 8

8 min

sampleImage_newsletter-february-2024
Encord Monthly Wrap: February Industry Newsletter

Hi there, Welcome the The Computer Vision Monthly Wrap Here’s what you should expect: 📦 YOLOv9 release with an explainer and code walkthrough on creating custom datasets. 📸 Meta’s V-JEPA for prediction video features. 📽️ Understanding Sora, OpenAI’s text-to-video model. ⚒️ Developer resources to learn how to analyze object detection model errors. ☁️ Computer vision case study from NVIDIA and Oracle. 🚀 Lessons from working with computer vision operations (CVOps) at scale. Let’s dive in! Top Picks for Computer Vision Papers This Month YOLOv9: Better than SoTA with Cutting-edge Real-time Object Detection If you haven’t heard yet, YOLOv9 is out, and, wow, it’s a high-performant model! YOLOv9 builds upon previous versions, using advancements in deep learning techniques and architectural design to beat state-of-the-art (SoTA) object detection tasks. What’s impressive? 🤯 It achieves top performance in object detection tasks on benchmark datasets like MS COCO. It surpasses existing real-time object detectors (YOLOv6, YOLOv8) in terms of accuracy, speed, and overall performance. It is much more adaptable to different scenarios and use cases. We have started seeing various applications, including surveillance, autonomous vehicles, robotics, and more. It is better than SoTA methods that use depth-wise convolution because it uses both the Programmable Gradient Information (PGI) and GLEAN (Generative Latent Embeddings for Object Detection) architectures. Read the paper on Arxiv. If that’s a lot, we also put out an explainer to help get to the important bits quickly with a walkthrough on using the open-source YOLOv9 release to create custom datasets. There’s also an accompanying repository for the implementation of the paper. Meta’s V-JEPA: Video Joint Embedding Predictive Architecture Explained In February, Meta released V-JEPA, a vision model exclusively trained using a feature prediction objective. In contrast to conventional machine learning methods, which rely on pre-trained image encoders, text, or human annotations, V-JEPA learns directly from video data without external supervision. What’s impressive? 👀 Instead of reconstructing images or relying on pixel-level predictions, V-JEPA prioritizes video feature prediction. This approach leads to more efficient training and superior performance in downstream tasks.  V-JEPA requires shorter training schedules than traditional pixel prediction methods (VideoMAE, Hiera, and OmniMAE) while maintaining high-performance levels. We wrote a comprehensive explainer of V-JEPA, including the architecture, key features, and performance details, in this blog post. Here is the accompanying repository on the implementation of V-JEPA. OpenAI Releases New Text-to-Video Model, Sora OpenAI responded to the recent debut of Google's Lumiere, a space-time diffusion model for video generation, by unveiling its own creation: Sora. The diffusion model can transform text descriptions into high-definition video clips for up to one minute. In this comprehensive explainer, you will learn: How Sora works Capabilities and limitations Safety considerations Other text-to-video generative models. Gemini 1.5: Google's Generative AI Model with 1 Million-Token Context Length and MoE Architecture Gemini 1.5 is a sparse mixture-of-experts (MoE) multimodal model with a context window of up to 1 million tokens in production and 10 million tokens in research. It excels at long-term recall and retrieval and generalizes zero-shot to long instructions, like analyzing 3 hours of video with near-perfect recall. Here is an explainer blog that distils the technical report with the necessary information. Developer Resources You’d Find Useful Multi-LoRA Composition for Image Generation → The space is moving so fast that it’s hard to miss out on gems like Multi-LoRA! The Multi-LoRA composition implementation integrates diverse elements like characters & clothing into a unified image to avoid the detail loss and distortion seen in traditional LoRA Merge. Check out the repo and try it yourself. Scaling MLOps for Computer Vision by MLOps.Community → In this panel conversation, experienced engineers talk about their experience, challenges, and best practices for working with computer vision operations (CVOps) at scale. How to Analyze Failure Modes of Object Detection Models for Debugging → This guide showcases how to use Encord Active to automatically identify and analyze the failure modes of a computer vision model to understand how well or poorly it performs in challenging real-world scenarios. NVIDIA Triton Server Serving at Oracle [Case Study] → I really liked this short case study by the Oracle Cloud team that discussed how their computer vision and data science services accelerate AI predictions using the NVIDIA Triton Inference Server. Some learnings in terms of cost savings and performance optimization are valuable. Here are other quick finds if you 💓 Encord and computer vision data stuff ⚡: Join the Encord Community to discuss this newsletter. Data-centric computer vision blog.

March 8

10 min

sampleImage_video-object-tracking-algorithms
Top 10 Video Object Tracking Algorithms in 2024

Object tracking has become a fundamental part of the computer vision ecosystem. It powers various modern artificial intelligence applications and is behind several revolutionary technologies, such as self-driving cars, surveillance, and action recognition systems. Tracking algorithms use a combination of object detection and object tracking to detect and localize entities within a video frame. These algorithms range from basic machine learning to complex deep learning networks. Each of these has different implementations and use cases. This article will discuss the top 10 most popular video object-tracking algorithms. It will go over video object-tracking algorithms' back-end implementations, advantages, and disadvantages. We will also explore popular computer vision applications for object tracking. What is Video Object Tracking? Video object tracking refers to detecting an object within a video frame and tracking its position throughout the video. The concept of object tracking stems from object detection, a popular computer vision (CV) technique used for identifying and localizing different objects in images. While object detection works on still images (single frames), video object tracking applies this concept to every frame in the video. It analyzes each frame to identify the object in question and draw a bounding box around it. The object is effectively tracked throughout the video by performing this operation on all frames. However, complex machine learning and deep learning algorithms apply additional techniques such as region proposal and trajectory prediction for real-time object inference. Object tracking algorithms have revolutionized several industries. It has enabled businesses to implement analytics and automation in various domains and led to applications like: Autonomous Vehicles: Tracking surrounding elements like pedestrians, roads, curbs. Automated Surveillance: Tracking people or illegal objects like guns and knives. Sports Analytics: Tracking the ball or players to create match strategies. Augmented Reality Applications: Tracking all objects in the visual field to superimpose the virtual elements. Customer Analysis in Retail: Tracking retail store customers to understand movement patterns and optimize shelf placement. Over the years, object tracking algorithms have undergone various improvements in-terms of accuracy and performance. Let’s discuss these in detail. Single-stage Object Detectors Vs. Two-stage Object Detectors Object detection is a crucial part of tracking algorithms. Hence, it is vital to understand them in detail. There are two main categories of object detectors: Single-stage and two-stage. Both these methodologies have proven to provide exceptional results. However, each offers different benefits, with the former having a lower inference time and the latter having better accuracy. Single-stage detectors perform faster since they rely on a single network to produce annotations. These models skip intermediate feature extraction steps, such as region proposal. They use the raw input image to identify objects and generate bounding box coordinates. One example of a single-stage detector is You only look once (YOLO). YOLO can generate annotations with a single pass of the image. Single Stage Vs. Two-Stage Detection Two-stage detectors, such as Fast R-CNN, comprise two networks. The first is a region proposal network (RPN) that analyzes the image and extracts potential regions containing the desired objects. The second network is a CNN-based feature extractor that analyzes the proposed regions. The latter identifies the objects present and outputs their bounding box coordinates.  Two-stage object detectors are computationally expensive compared to their single-stage counterparts. However, they produce more accurate results. Object Tracking Approaches Object tracking algorithms work on two granularity levels. These include: Single Object Tracking (SOT) SOT is used to track the location of a single object throughout the video feed. These detection-free algorithms depend on the user to provide a bounding box around the target object on the first frame. The algorithm learns to track the position and movement of the object present within the box. It localizes the object's shape, posture, and trajectory in every subsequent frame. Single object tracking is useful when the focus must be kept on a particular entity. Some examples include tracking suspicious activity in surveillance footage or ball-tracking in sports analytics. Popular SOT algorithms include Particle Filters and Siamese Networks. However, one downside of traditional SOT algorithms is that they are unsuitable for context-aware applications where tracking multiple objects is necessary. Multiple Object Tracking (MOT) MOT works on the same concept as SOT. However, multi-object tracking identifies and tracks multiple objects throughout a video instead of a single object. MOT algorithms use extensive training datasets to understand moving objects. Once trained, they can identify and track multiple objects within each frame. Modern deep-learning MOT algorithms, like DeepSORT, can even detect new objects mid-video and create new tracks for them while keeping existing tracks intact. Multiple-object tracking is useful when various objects must be analyzed simultaneously. For example, in virtual reality (VR) applications, the algorithm must keep track of all objects in the frame to superimpose the virtual elements. However, these algorithms are computationally expensive and require lengthy training time. Phases of Object Tracking Process Visual object tracking is a challenging process comprising several phases. Target Initialization: The first step is to define all the objects of interest using labels and bounding boxes. The annotations, which include the names and locations of all the objects to be tracked, are specified in the first video frame. The algorithm then learns to identify these objects in all the subsequent images or video sequences. Appearance Modelling: An object may undergo visual transformation throughout the video due to varying lighting conditions, motion blur, image noise, or physical augmentations. This phase of the object-tracking process aims to capture these various transformations to improve the model’s robustness. It includes constructing object descriptions and mathematical models to identify objects with different appearances. Motion Estimation: Once the object features are defined, motion estimation predicts the object’s position based on the previous frame data. This is achieved by leveraging linear regression techniques or Particle Filters. Target Positioning: Motion estimation provides an estimate of the object's position. The next step is to pinpoint the exact coordinates within the predicted region. This is accomplished using a greedy search, i.e., checking every possibility or a maximum posterior estimation that looks at the most likely place using visual clues. Criteria for Selecting a Video Object Tracking Algorithm The two primary criteria to evaluate object tracking methods are accuracy and inference time. These help determine the best algorithm for particular use cases. Let’s discuss these criteria in detail. Accuracy Tracking algorithms output two main predictions: object identity (label) and location (bounding box coordinates). The accuracy of these models is determined by evaluating both these predictions and analyzing how well it was able to identify and localize the object. Metrics like Accuracy, Precision, Recall, and F1-score help evaluate the model's ability to classify the found object. While accuracy provides a generic picture, precision, and recall judge the model based on occurrences of false positives and negatives. Metrics like intersection-over-union (IoU) are used for localization accuracy. IoU calculates how much the predicted bounding box coincides with its ground truth value. A higher value means higher intersection and, hence, higher accuracy. Intersection Over Union (IoU) Inference Time The second judgment criterion is the speed of inference. Inference time determines how quickly the algorithm processes a video frame and predicts the object label and location. It is often measured in frames-per-second (FPS). This refers to the amount of frames the algorithm can process and output every second. A higher FPS value indicates faster inference. Challenges in Object Tracking Object tracking techniques carry various benefits for different industries. However, implementing a robust tracking algorithm is quite challenging. Some key challenges include: Object Variety: The real world comes with countless objects. Training a generic tracking algorithm would require an extensive dataset containing millions of objects. For this reason, object tracking models are generally domain-specific, with even the vastest models trained on only a few thousand objects. Varying Conditions: Besides the object variety, the training data must also cover objects in different conditions. A single object must be captured in different lighting conditions, seasons, times of day, and from different camera angles. Varying Image Quality: Images from different lenses produce varying information in terms of color production, saturation, etc. A robust model must incorporate these variations to cover all real-world scenarios. Computation Costs: Handling large image or video datasets requires considerable expertise and computational power. Developers need access to top-notch GPUs and data-handling tools, which can be expensive. Training deep-learning-based tracking algorithms can also increase operational costs if you use paid platforms that charge based on data units processed.  Scalability: Training general-purpose object tracking models requires extensive datasets. The growing data volumes introduce scalability challenges as developers require platforms that can handle increasingly large volumes of data and can increase computation power to train larger complex models. Top Algorithms for Video Object Tracking Here is a list of popular object tracking algorithms, ranging from simple mathematical models to complex deep learning architectures. Kalman Filter Kalman filters estimate an object’s position and predict its motion in subsequent frames. They maintain an internal representation of the object's state, including its position, velocity, and sometimes acceleration. The filters use information from the object’s previous state and a mathematical model analyzing the object’s motion to predict a future state. The model accounts for any uncertainty in the object's motion (noise). It incorporates all the discussed factors and estimates the object’s current state to create a future representation. Advantages It is a mathematical model that does not require any training. It is computationally efficient. Disadvantage Subpar performance and capabilities compared to modern deep learning algorithms. The model works on various assumptions, such as constant object acceleration. The algorithm does not perform well in random motion scenarios. KCF (Kernelized Correlation Filters) KCF is a mathematical model that understands object features and learns to distinguish them from their background. It starts with the user providing a bounding box around the object in the first frame. Once feature understanding is complete, it uses correlation filters based on the kernel trick to construct a high-dimensional relationship between the features and the true object. It uses the correlation features in subsequent frames to scan around the object's last known location. The area with the highest correlation is predicted to contain the object. Advantages Fast Computation. Low Memory Requirement. Competitive Results in general cases. Disadvantages Traditional KCF faces challenges in conditions such as varying object scales or objects touching frame boundaries. DeepSORT The Deep Simple Online Realtime Tracking (DeepSORT) algorithm extends the original SORT algorithm. The original SORT algorithm used Kalman filters to predict object motion and the Hungarian algorithm for frame-by-frame data association. However, this algorithm struggles with occlusions and varying camera angles and can lose object tracking in such complex scenarios. DeepSORT Architecture DeepSORT uses an additional convolutional neural network (CNN) as a feature extractor. These are called appearance features as they learn to determine the object identity (appearance) in different scenarios and allow the algorithm to distinguish between moving objects. DeepSORT combines the information from Filtering and CNN to create a deep association metric for accurate detection. Advantages DeepSort’s simple yet efficient implementation provides real-time performance. The model is modular. It can support any detection network of the user's choice, such as YOLO or SSD. It maintains its detection during occluded environments and can distinguish between different objects in complex scenarios. Disadvantages Offline training of a separate detection network can be challenging and requires an extensive dataset for high accuracy. FairMOT The fair multi-object tracking (FairMOT) algorithm uses a pre-trained model like faster R-CNN for detecting objects in the video sequence. It then uses a neural network to extract features from the detected object. FairMOT Architecture These features are used to track the object across other frames. The branches share the same underlying architecture and receive equal weightage during training. The FairMOT algorithm treats all classes fairly and provides a balanced performance between the two tasks: detection and tracking. Advantages Provides balanced performance between tracking and detection. Improved tracking accuracy due to the re-identification branch (feature extraction branch) Disadvantage Computationally expensive due to the two neural network branches being trained. MDNet The multi-domain network (MDNet) is popular for learning across different domains. It consists of two modules. The first is a CNN architecture shared amongst all the video sequences, i.e., it is domain-independent and learns from the entire dataset. This consists of CNN layers and a few flattened, fully connected layers. MDNet Architecture The second part comprises parallel fully connected (FC) layers, each processing domain-specific information. If the data captures information from 5 domains, the second portion will have 5 FC layers. Each of these layers is independently updated during back-propagation depending on the domain of the target image. Advantages Excellent performance across different domains. The domain-specific branches can be fine-tuned on the fly if significant domain shifts are detected. Disadvantages If data is imbalanced, the model will display uneven performance across the different domains. YOLOv8 (You Only Look Once) YOLOv8 is a single stage-detector that ranks among the most popular object tracking algorithms. The YOLO family of models is based on a CNN architecture that learns to predict object labels and positions with a single pass of the image. Yolov8 Tasks Catalog The model v8 follows a similar architecture to its predecessor and consists of various CNN and fully connected layers. It is an anchor-free algorithm, which directly predicts the object’s center rather than an offset from a predefined anchor. Moreover, the algorithm can be used for classification, segmentation, pose estimation, object detection, and tracking. YOLOv8 extends its detection capabilities by providing a range of trackers. Two popular options amongst these are Bot-SORT and ByteTrack. All the trackers are customizable, and users can fine-tune parameters like confidence threshold and tracking area. Advantages The model covers various use cases, including tracking and segmentation. High accuracy and performance. Easy Python Interface. Disadvantages Trouble detecting small objects. YOLOv8 provides various model sizes, each trading performance for accuracy. Here’s all you need to know about the YOLO family of model. Read more about YOLO models for Object Detection Explained [YOLOv8 Updated] Siamese Neural Networks (SNNs) Siamese-based tracking algorithms consist of two parallel branches of neural networks. One is a template branch, which contains the template image (including the object bounding box information) and the next frame where the object is to be found. This branch consists of CNNs and pooling layers and extracts features from both images, such as edges, texture, and shape. A fully convolutional siamese network for object tracking The other is the similarity branch that takes the features from the template and search image. It calculates the similarity between the two images using algorithms like contrastive loss. The output of this network is the likelihood of the object being present at different positions in the image. The Siamese network has had various advancements over the years. The modern architectures include attention mechanisms and RPNs for improved performance. Advantages Multiple advancements, including SiamFC, SiamRPN, etc. Disadvantages Training two parallel networks leads to long training times. GOTURN (Generic Object Tracking Using Regression Networks) Generic Object Tracking Using Regression Networks (GOTURN) is a deep learning based offline learning algorithm. The framework accepts two images, a previous frame and a current frame. The previous frame contains the object at its center, and the image is cropped to 2 times the bounding box size. The current frame is cropped in the same location, but the object is off-center as it has supposedly moved from its position. GOTURN High-level Architecture The internal structure of the model consists of convolutional layers taken from the CaffeNet architecture. Each of the two frames is passed through these layers, and the output is concatenated and processed through a series of fully connected layers. The objective of the network is to learn features from the previous frame and predict the bounding box in the current. Advantages Excellent performance, even on CPU. Disadvantages Troubled in scenarios where only some part of the object is visible. Object tracking is highly affected by imbalanced training data. Want to know more about creating a fair dataset? Read more about Balanced and Imbalanced Datasets in Machine Learning [Full Introduction] TLD (Tracking, Learning, and Detection) TLD is a framework designed for long-term tracking of an unknown object in a video sequence. The three components serve the following purpose: Tracker: Predicts the object location in the next frame using information in the current frame. This module uses techniques like mean-shift or correlation filtering. Detector: Scans the input frame-by-frame for potential objects using previously learned object appearances. Learning: Observes the tracker's and the detector's performance and identifies their errors. It further generates training samples to teach the detector to avoid mistakes in the future. Tracking-Learning-Detection Advantages Real-time performance. Disadvantages Sensitive to illumination changes. Can lose track of the object if it is completely occluded in any frame. Can fail if the object appearance changes mid-video. Median Flow Tracker The Median Flow Tracker predicts object movement in videos by analyzing feature points across frames. It estimates optical flow, filters out unreliable measurements, and uses the remaining data to update the object's bounding box. Tracking using Median Flow Internally, it tracks motion in both forward and backward directions and compares the two trajectories. Advantages Works well for predictable motion. Disadvantage Fails in scenarios of abrupt and random motion. Applications of Video Object Tracking Video Object Tracking has important use-cases in various industries. These use-cases automate laborious tasks and provide critical analytics. Let's discuss some key applications. Autonomous Vehicles Market leaders like Tesla, Waymo, and Baidu are constantly enhancing their AI infrastructure with state-of-the-art algorithms and hardware for improved tracking. Modern autonomous vehicles use different cameras and robust neural processing engines to track the objects surrounding them. Video object tracking plays a vital role in mapping the car's surroundings. This feature map helps the vehicle distinguish between elements such as trees, roads, pedestrians, etc. Autonomous Harvesting Robots Object tracking algorithms also benefit the agriculture industry by allowing autonomous detection and harvesting of ready crops. Agri-based companies like Four Growers use detection and tracking algorithms to identify harvestable tomatoes and provide yield forecasting. They use the Encord annotation tool and a team of professional annotators to label millions of objects simultaneously. Using AI-assisted tools has allowed them to cut the data processing time by half. Sports Analytics Sports analysts use computer vision algorithms to track player and ball movement to build strategies. Video tracking algorithms allow the analysts to understand player weaknesses and generate AI-based analytics. The tracking algorithms can also be used to fix player postures to improve performance and mitigate injury risks. Traffic Congestion & Emission Monitoring System Computer vision is used to track traffic activity on roads and airports. The data is also used to manage traffic density and ensure smooth flow. Companies like Automotus use object tracking models to monitor curb activity and reduce carbon emissions. Their solution automatically captures the time a car spends on the curb, detects any traffic violations, and analyzes driver behavior. Vascular Ultrasound Analysis Object detection has various use cases in the healthcare domain. One of the more prominent applications is Ultrasound analysis for diagnosing and managing vascular diseases like Popliteal Artery Aneurysms (PAAs). CV algorithms help medical practitioners in detecting anomalous entities in medical imaging. The automated detection allows for further AI analysis, such as classification, and allows the detection of minute irregularities that might otherwise be ignored. Professional Video Editing Professional tools like Adobe Premiere Pro use object tracking to aid professional content creators. It allows creators to apply advanced special effects on various elements and save time creating professional edits. Customer Analysis in Retail Stores Tracking algorithms are applied in retail stores via surveillance cameras. They are used to detect and track customer movement throughout the store premises. The tracking data helps the store owner understand hot spots where the customers spend the most time. It also gives insights into customer movement patterns that help optimize product placement on shelves. Want to know the latest computer vision use cases? Learn more about the ten most exciting applications of computer vision in 2024 Video Object Tracking: Key Takeaways The computer vision domain has come a long way, and tasks like classification, segmentation, and object tracking have seen significant improvements. ML researchers have developed various algorithms for video object tracking, each of which holds certain benefits over the other. In this article, we discussed some of the most popular architectures. Here are a few takeaways: Object Tracking vs. Object Detection: Video object tracking is an extension of object detection and applies the same principles to video sequences. Multiple Categories of Object Tracking: Object tracking comprises various sub-categories, such as single object tracking, multiple object tracking, single-stage detection, and two-stage detection. Object Tracking Metrics: Object tracking algorithms are primarily judged on their inference time (frames-per-second) and tracking accuracy. Popular Frameworks: Popular tracking frameworks include YOLOv8, DeepSORT, GOTURN, and MDNet. Applications: Object tracking is used across various domains, including healthcare, autonomous vehicles, customer analysis, and sports analytics.

March 8

10 min

sampleImage_model-validation-tools
Top 9 Tools for Generative AI Model Validation in Computer Vision

The integrity, diversity, and reliability of the content that AI systems generate depend on generative AI model validation. It involves using tools to test, evaluate, and improve these models. Validation is important for detecting biases, errors, and potential risks in AI-generated outputs and for facilitating their rectification to adhere to ethical and legal guidelines. The demand for robust validation tools is increasing with the adoption of generative AI models. This article presents the top 9 tools for generative AI model validation. These tools help identify and correct discrepancies in generated content to improve model reliability and transparency in AI applications. The significance of model validation tools cannot be overstated, especially as generative AI continues to become mainstream. These tools are critical to the responsible and sustainable advancement of generative AI because they ensure the quality and integrity of AI-generated content. Here’s the list of tools we will cover in this article: Encord Active DeepChecks HoneyHive Arthur Bench Galileo LLM Studio TruLens Arize Weights and Biases HumanLoop Now that we understand the importance of optimizing performance in generative AI models, let's delve into the guidelines or criteria that can help us evaluate different tools and help us achieve these goals.  Criteria for Evaluating Generative AI Tools In recent years, generative AI has witnessed significant advancements, with pre-trained models as a cornerstone for many breakthroughs. Evaluating generative AI tools involves comprehensively assessing their quality, robustness, and ethical considerations.  Let’s delve into the key criteria for evaluating the generative AI tools: Scalability and Performance: Assess how well the tool handles increased workloads. Can it scale efficiently without compromising performance? Scalability is crucial for widespread adoption. Model Evaluation Metrics: Consider relevant metrics such as perplexity, BLEU score, or domain-specific measures. These metrics help quantify the quality of the generated content. Support for Different Data Types: Generative AI tools should handle various data types (text, images, videos, etc.). Ensure compatibility with your specific use case. Built-in Metrics to Assess Sample Quality: Tools with built-in quality assessment metrics are valuable. These metrics help measure the relevance, coherence, and fluency of the generated content. Interpretability and Explainability: Understand how the model makes decisions. Transparent models are easier to trust and debug. Experiment Tracking: Effective experiment tracking allows you to manage and compare different model versions. It's essential for iterative improvements. Usage Metrics: Understand how real users interact with the model over time. Usage metrics provide insights into adoption, engagement, and user satisfaction. Remember that generative AI is unique, and traditional evaluation methods may need adaptation. By focusing on these criteria, organizations can fine-tune their generative AI projects and drive successful results both now and in the future. Encord Active Encord Active is a data-centric model validation platform that allows you to test your models and deploy into production with confidence. Inspect model predictions and compare to your Ground Truth, surface common issue types and failure environments, and easily communicate errors back to your labeling team in order to validate your labels for better model performance. By emphasizing real data for accuracy and efficiency, Encord Active ensures foundation models are optimized and free from biases, errors, and risks. The Model Evaluation & Data Curation Toolkit to Build Better Models Key Features Let’s evaluate Encord Active based on the specified criteria: Scalability and Performance: Encord Active ensures robust model performance and adaptability as data landscapes evolve. Model Evaluation Metrics: The tool provides robust model evaluation capabilities, uncovering failure modes and issues. Built-in Metrics to Assess Sample Quality: It automatically surfaces label errors and validates labels for better model performance. Interpretability and Explainability: Encord Active offers explainability reports for model decisions. Experiment Tracking: While not explicitly mentioned, it likely supports experiment tracking. Usage Metrics: Encord Active helps track usage metrics related to data curation and model evaluation. Semantic Search: Encord Active is a data-centric AI platform that uses a built-in CLIP to index images from Annotate. The indexing process involves analyzing images and textual data to create a searchable representation that aligns images with potential textual queries. This provides an in-depth analysis of your data quality.Semantic search with Encord Active can be performed in two ways. Either through text-based queries by searching your images with natural language, or through Reference or anchor image by searching your images using a reference or anchor image. The guide recommends using Encord Annotate to create a project and import the dataset, and Encord Active to search data with natural language.  Best for Encord Active is best suited for ML practitioners deploying production-ready AI applications, offering data curation, labeling, model evaluation, and semantic search capabilities all in one. Learn about how Automotus increased mAP 20% while labeling 35% less of their dataset with Encord Active.   Pricing Encord Active OS is an open-source toolkit for local installation. Encord Active Cloud (an advanced and hosted version) has a pay-per-user model. Get started here.  Deepchecks Deepchecks is an open-source tool designed to support a wide array of language models, including ChatGPT, Falcon, LLaMA, and Cohere.  DeepChecks Dashboard Key Features and Functionalities Scalability and Performance: Deepchecks ensures validation for data and models across various phases, from research to production. Model Evaluation Metrics: Deepchecks provides response time and throughput metrics to assess model accuracy and effectiveness. Interpretability and Explainability: Deepchecks focuses on making model predictions understandable by associating inputs with consistent outputs. Usage Metrics: Deepchecks continuously monitors models and data throughout their lifecycle, customizable based on specific needs. Open-Source Synergy: Deepchecks supports both proprietary and open-source models, making it accessible for various use cases. Best for Deepchecks is best suited for NLP practitioners, researchers, and organizations seeking comprehensive validation, monitoring, and continuous improvement of their NLP models and data.  Pricing The pricing model for Deepchecks is based on the application count, seats, daily estimates and support options. The plans are categorized into Startup, Scale and Dedicated. HoneyHive HoneyHive is a platform with a suite of features designed to ensure model accuracy and reliability across text, images, audio, and video outputs. Adhering to NIST's AI Risk Management Framework provides a structured approach to managing risks inherent in non-deterministic AI systems, from development to deployment. HoneyHive - Evaluation and Observability for AI Applications Key Features and Functionalities Scalability and Performance: HoneyHive enables teams to deploy and continuously improve LLM-powered products, working with any model, framework, or environment. Model Evaluation Metrics: It provides evaluation tools for assessing prompts and models, ensuring robust performance across the application lifecycle. Built-in Metrics for Sample Quality: HoneyHive includes built-in sample quality assessment, allowing teams to monitor and debug failures in production. Interpretability and Explainability: While not explicitly mentioned, HoneyHive’s focus on evaluation and debugging likely involves interpretability and explainability features. Experiment Tracking: HoneyHive offers workspaces for prompt templates and model configurations, facilitating versioning and management. Usage Metrics: No explicit insights into usage patterns and performance metrics. Additional Features Model Fairness Assessment: Incorporate tools to evaluate model fairness and bias, ensuring ethical and equitable AI outcomes. Automated Hyperparameter Tuning: Integrate hyperparameter optimization techniques to fine-tune models automatically. Best for HoneyHive.ai is best suited for small teams building Generative AI applications, providing critical evaluation and observability tools for model performance, debugging, and collaboration. Pricing HoneyHive.ai offers a free plan for individual developers. Arthur Bench An open-source evaluation tool for comparing LLMs, prompts, and hyperparameters for generative text models, the Arthur Bench open-source tool will enable businesses to evaluate how different LLMs will perform in real-world scenarios so they can make decisions when integrating the latest AI technologies into their operations. Arthur Bench’s comparison of the hedging tendencies in various LLM responses  Key Features and Functionalities Scalability and Performance: Arthur Bench evaluates large language models (LLMs) and allows comparison of different LLM options. Model Evaluation Metrics: Bench provides a full suite of scoring metrics, including summarization quality and hallucinations. Built-in Metrics to Assess Sample Quality: Arthur Bench offers metrics for assessing accuracy, readability, and other criteria. Interpretability and Explainability: Not explicitly mentioned Experiment Tracking: Bench allows teams to compare test runs. Usage Metrics: Bench is available as both a local version (via GitHub) and a cloud-based SaaS offering, completely open source. Additional Features Customizable Scoring Metrics: Users can create and add their custom scoring metrics. Standardized Prompts for Comparison: Bench provides standardized prompts designed for business applications, ensuring fair evaluations. Best for The Arthur Bench tool is best suited for data scientists, machine learning researchers, and teams comparing large language models (LLMs) using standardized prompts and customizable scoring metrics.  Pricing Arthur Bench is an open-source AI model evaluator, freely available for use and contribution, with opportunities for monetization through team dashboards. Galileo LLM Studio Galileo LLM Studio is a platform designed for building production-grade Large Language Model (LLM) applications, providing tools for ensuring that LLM-powered applications meet standards. The tool supports local and cloud testing. Galileo LLM Studio  Key Features and Functionalities  Scalability and Performance: Galileo LLM Studio is a platform for building Large Language Model (LLM) applications. Model Evaluation Metrics: Evaluate, part of LLM Studio, offers out-of-the-box evaluation metrics to measure LLM performance and curb unwanted behavior or hallucinations. Built-in Metrics to Assess Sample Quality: LLM Studio’s Evaluate module includes metrics to assess sample quality. Interpretability and Explainability: Not explicitly mentioned. Experiment Tracking: LLM Studio allows prompt building, version tracking, and result collaboration. Usage Metrics: LLM Studio’s Observe module monitors productionized LLMs. Additional Features Here are some additional features of Galileo LLM Studio: Generative AI Studio: Users build, experiment and test prompts to fine-tune model behavior, to improve the relevance and model efficiency by exploring the capabilities of generative AI NLP Studio: Galileo supports natural language processing (NLP) tasks, allowing users to analyze language data, develop models, and work on NLP tasks. This integration provides a unified environment for both generative AI and NLP workloads. Best for Galileo LLM Studio, is a specialized platform tailored for individuals working with Large Language Models (LLMs) because it provides necessary tools specifically designed for LLM development, optimization and validation.  Pricing The pricing model for Galileo GenAI Studio is based on two predominant models: Consumption: This pricing model is usually measured per thousand tokens used. It allows users to pay based on their actual usage of the platform. Subscription: In this model, pricing is typically measured per user per month. Users pay a fixed subscription fee to access the platform’s features and services. TruLens TruLens enables the comparison of generated outputs to desired outcomes to identify discrepancies. Advanced visualization capabilities provide insights into model behavior, strengths, and weaknesses. TruLens for LLMs Key Features and Functionalities Scalability and Performance: TruLens evaluates large language models (LLMs) and scales up experiment assessment. Model Evaluation Metrics: TruLens provides feedback functions to assess LLM app quality, including context relevance, groundedness, and answer relevance. Built-in Metrics to Assess Sample Quality: TruLens offers an extensible library of built-in feedback functions for identifying LLM weaknesses. Interpretability and Explainability: Not explicitly emphasized Experiment Tracking: TruLens allows tracking and comparison of different LLM apps using a metrics leaderboard. Usage Metrics: TruLens is versatile for various LLM-based applications, including retrieval augmented generation (RAG), summarization, and co-pilots. Additional Features Customizable Feedback Functions: TruLens allows you to define your custom feedback functions to tailor the evaluation process to your specific LLM application. Automated Experiment Iteration: TruLens streamlines the feedback loop by automatically assessing LLM performance, enabling faster iteration and model improvement.  Best for  TruLens for LLMs is suited for natural language processing (NLP) researchers, and developers who work with large language models (LLMs) and want to rigorously evaluate their LLM-based applications.  Pricing TruLens is an open-source model and is thus free and available for download.  Arize Arize AI is designed for model observability and LLM (Language, Learning, and Modeling) evaluation. It helps monitor and assess machine learning models, track experiments, offer automatic insights, heatmap tracing, cohort analysis, A/B comparisons  and ensure model performance and reliability. Arize Dashboard Key Features and Functionalities Scalability and Performance: Arize AI handles large-scale deployments and provides real-time monitoring for performance optimization. Model Evaluation Metrics: Arize AI offers a comprehensive set of evaluation metrics, including custom-defined ones. Sample Quality Assessment: It monitors data drift and concept drift to assess sample quality. Interpretability and Explainability: Arize AI supports model interpretability through visualizations. Experiment Tracking: Users can track model experiments and compare performance. Usage Metrics: Arize AI provides insights into model usage patterns. Additional Features ML Observability: Arize AI surfaces worst-performing slices, monitors embedding drift, and offers dynamic dashboards for model health. Task-Based LLM Evaluations: Arize AI evaluates task performance dimensions and troubleshoots LLM traces and spans. Best for Arize AI helps business leaders pinpoint and resolve model issues quickly. Arize AI is for anyone who needs model observability, evaluation, and performance tracking.  Pricing Arize AI offers three pricing plans: Free Plan: Basic features for individuals and small teams. Pro Plan: Suitable for small teams, includes more models and enhanced monitoring features. Enterprise Plan: Customizable for larger organizations with advanced features, and tailored support.  Weights and Biases Weights and Biases enables ML professionals to track experiments, visualize performance, and collaborate effectively. Logging metrics, hyperparameters, and training data facilitate comparison and analysis. Using this tool, ML practitioners gain insights, identify improvements, and iterate for better performance. Weights & Biases: The AI Developer Platform Key Features and Functionalities Scalability and Performance: W&B helps AI developers build better models faster by streamlining the entire ML workflow, from tracking experiments to managing datasets and model versions. Model Evaluation Metrics: W&B provides a flexible and tokenization-agnostic interface for evaluating auto-regressive language models on various Natural Language Understanding (NLU) tasks, supporting models like GPT-2, T5, Gpt-J, Gpt-Neo, and Flan-T5. Built-in Metrics to Assess Sample Quality: While not explicitly mentioned, W&B’s evaluation capabilities likely include metrics to assess sample quality, given its focus on NLU tasks. Interpretability and Explainability: W&B does not directly provide interpretability or explainability features, but it integrates with other libraries and tools (such as Fastai) that may offer such capabilities. Experiment Tracking: W&B allows experiment tracking, versioning, and visualization with just a few lines of code. It supports various ML frameworks, including PyTorch, TensorFlow, Keras, and Scikit-learn. Usage Metrics: W&B monitors CPU and GPU usage in real-time during model training, providing insights into resource utilization. Additional Features  Panels: W&B provides visualizations called “panels” to explore logged data and understand relationships between hyperparameters and metrics. Custom Charts: W&B enables the creation of custom visualizations for analyzing and interpreting experiment results. Best for Weights & Biases (W&B) is best suited for machine learning practitioners and researchers who need comprehensive experiment tracking, visualization, and resource monitoring for their ML workflows. Pricing The Weights & Biases (W&B) AI platform offers the following pricing plans: Personal Free: Unlimited experiments, 100 GB storage, and no corporate use allowed. Teams: Suitable for teams, includes free tracked hours, additional hours billed separately. Enterprise: Custom plans with flexible deployment options, unlimited tracked hours, and dedicated support. HumanLoop HumanLoop uses HITL (Human In The Loop), allowing collaboration between human experts and AI systems for accurate and quality outputs. By facilitating iterative validation, models improve with real-time feedback. With expertise from leading AI companies, HumanLoop offers a comprehensive solution for validating generative AI models. Humanloop: Collaboration and evaluation for LLM applications Key Features and Functionalities Scalability and Performance: Humanloop provides a collaborative playground for managing and iterating on prompts across your organization, ensuring scalability while maintaining performance. Model Evaluation Metrics: It offers an evaluation and monitoring suite, allowing you to debug prompts, chains, or agents before deploying them to production. Built-in Metrics to Assess Sample Quality: Humanloop enables you to define custom metrics, manage test data, and integrate them into your CI/CD workflows for assessing sample quality. Interpretability and Explainability: While Humanloop emphasizes interpretability by allowing you to understand cause and effect, it also ensures explainability by revealing hidden parameters in deep neural networks. Experiment Tracking: Humanloop facilitates backtesting changes and confidently updating models, capturing feedback, and running quantitative experiments. Usage Metrics: It provides insights into testers’ productivity and application quality, helping you make informed decisions about model selection and parameter tuning. Additional Features Best-in-class Playground: Humanloop helps developers manage and improve prompts across an organization, fostering collaboration and ensuring consistency. Data Privacy and Security: Humanloop emphasizes data privacy and security, allowing confident work with private data while complying with regulations. Best for The Humanloop tool is particularly well-suited for organizations and teams that require collaborative AI validation, model evaluation, and experiment tracking, making it an ideal choice for managing and iterating on prompts across different projects. Its features cater to both technical and non-technical users, ensuring effective collaboration and informed decision-making in the AI development and evaluation process.  Pricing  Free Plan allows for Humanloop AI product prototyping for 2 members with 1,000 logs monthly and community support.  Enterprise Plan includes enterprise-scale deployment features and priority assistance. Generative AI Model Validation Tools: Key Takeaways Model validation tools ensure reliable and accurate AI-generated outputs, enhancing user experience, and fostering trust in AI technology.  Adaptation of these tools to evolving technologies is needed to provide real-time feedback, prioritizing - transparency, accountability, and fairness to address bias and ethical implications in AI-generated content.  The choice of a tool should consider scalability, performance, model evaluation metrics, sample quality assessment, interpretability, experiment tracking, and usage metrics.  Generative AI Validation Importance:  The pivotal role of generative AI model validation ensures content integrity, diversity, and reliability, emphasizing its significance in adhering to ethical and legal guidelines. Top Tools for Model Validation: Different tools are available catering to diverse needs, helping identify and rectify biases, errors, and discrepancies in AI-generated content, essential for model transparency and reliability. Criteria for Tool Evaluation: The key criteria for evaluating generative AI tools are focusing on scalability, model evaluation metrics, sample quality assessment, interpretability, and experiment tracking to guide organizations in choosing effective validation solutions. Adaptation for Generative AI: Recognizing the uniqueness of generative AI, the article emphasizes the need for adapting traditional evaluation methods. By adhering to outlined criteria, organizations can fine-tune generative AI projects for sustained success, coherence, and reliability.

March 6

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Software To Help You Turn Your Data Into AI

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