At Encord, our mission is to accelerate the development and democratization of quality AI and computer vision applications by providing tools which enable actionable insights across your data, labels and models. Today, we’re bringing that one step further announcing our product launch integrating Meta’s Segment Anything Model (SAM) into the Encord Annotate platform. SAM, or the Segment Anything Model, is Meta’s new zero-shot foundation model in computer vision, a cornerstone of their Segment Anything project. As a zero-shot foundation model, and as its name suggest, SAM is immediately capable of "segmenting anything" including image data it hasn't seen before, from a simple combination of keypoints and, if you wish, a delimiting bounding box. {{light_callout_start}} For all the details of the inner workings and greater significance of SAM, check out our SAM explainer. {{light_callout_end}} The release last week set the internet ablaze with possibilities, those both obvious and those yet to come. We’re here to tell you about the possibilities available now. Integrating SAM with Encord Annotate pairs the power of SAM to segment anything with Encord’s powerful ontologies, interactive editor, and comprehensive media support. Encord supports using SAM to annotate images and videos, as well as speciality data types such as satellite and DICOM data. DICOM support includes X-ray, CT, and MRI among others — with no additional effort from you. Our powerful labeling tool gives you an interactive editor experience allowing you to define regions to include and exclude, producing both bounding boxes and segmentations to your exact specification. Of course, integrating with Encord means you can take advantage of our annotation workflows as well — ensuring you get all the benefits of a collaborative annotation and review platform powered by AI-assisted labeling and our annotator training module. We’re very excited to bring SAM to Encord to support your AI initiatives - get started here. You can also check out our tutorial on how to fine-tune Segment Anything here. {{SAM_CTA}}

Are you looking for a video annotation tool for your computer vision project? Look no further! We've compiled a list of the top eight best video annotation tools, complete with their use cases, benefits, key features, and pricing. Deciding on the right video annotation toolkit for your needs depends on several factors, including whether you have vast amounts of unlabeled data and whether manual annotation is too time-consuming and expensive. With a powerful video annotation tool, you can automate and accelerate the process. Our list is designed for data ops teams looking to manage in-house or outsourced annotators, CTOs hoping to reduce the cost of manual annotation, and data scientists and ML engineers in search of a solution to automate annotations and labeling while identifying potential edge cases and outliers. {{light_callout_start}} Working with images? Check out our 9 Best Image Annotation Tools for Computer Vision instead! {{light_callout_end}} Top 8 Video Annotation Tools for Computer Vision Encord LabelMe CVAT SuperAnnotate Dataloop Supervisely Scale Img Lab  Let’s dive in ... Encord Encord's collaborative video annotation platform helps you label video training data more quickly, build active learning pipelines, create better-quality datasets and accelerate the development of your computer vision models. Encord's suite of features and toolkits includes an automated video annotation platform that will help you 6x the speed and efficiency of model development. Encord is a powerful solution for teams that:  Need a native-enabled video annotation platform with features that make it easy to automate the end-to-end management of data labeling, QA workflows, and automated AI-powered annotation Want to accelerate their computer vision model development, making video annotation 6x faster than manual labeling. Benefits & key features:  Encord is a state-of-the-art AI-assisted labeling and workflow tooling platform powered by micro-models, ideal for video annotation, labeling, QA workflows, and training computer vision models Built for computer vision, with native support for numerous annotation types, such as bounding box, polygon, polyline, instance segmentation, keypoints, classification, and much more  As a computer vision toolkit, it supports a wide-range of native and visual modalities for video annotation and labeling, including native video file format support (e.g., full-length videos, and numerous file formats, including MP4 and WebM) Automated, AI-powered object tracking means your annotation teams can annotate videos 6x faster than manual processes Assess and rank the quality of your video-based datasets and labels against pre-defined or custom metrics, including brightness, annotation duplicates, occlusions in video or image sequences, frame object density, and numerous others  Evaluate training datasets more effectively using a trained model and imported model predictions with acquisition functions such as entropy, least confidence, margin, and variance with pre-built implementations Manage annotators collaboratively and at scale with customizable annotator and data management dashboards Best for:  ML, data ops, and annotation teams looking for a video annotation tool that will accelerate model development. Data science and operations teams that need a solution for collaborative end-to-end management of outsourced video annotation work. Pricing:  Start with a free trial or contact sales for enterprise plans. Further reading:  The Complete Guide to Image Annotation for Computer Vision 4 Ways to Debug Computer Vision Models [Step By Step Explainer] Closing the AI Production Gap with Encord Active Active Learning in Machine Learning: A Comprehensive Guide {{product_sam_cta}} LabelMe LabelMe is an open-source online annotation tool developed by the MIT Computer Science and Artificial Intelligence Laboratory. It includes the downloadable source code, a toolbox, an open-source version for 3D images, and image datasets you can train computer vision models on. LabelMe Benefits & key features:  LabelMe includes a dataset you can use to train models on, and you can use the LabelMe Matlab toolbox to annotate and label them (here’s the Github repository for this) It also comes with a 3D database with thousands of images of everyday scenes and object categories  You can also outsource annotation using Amazon Mechanical Turk, and LabelMe encourages this here. Best for:  ML and annotation teams. Although, given the open-source nature of LabelM and the database, it may be more effective and useful for academic rather than commercial computer vision projects.  Pricing:  Free, open-source.  CVAT CVAT (Computer Vision Annotation Tool) started life as an Intel application that they made open-source, thanks to an MIT license. Now it operates as an independent company and foundation, with Intel’s continued support under the OpenCV umbrella.  CVAT.org has moved to its new home, at CVAT.ai.  CVAT Benefits & key features:  CVAT is now part of an extensive OpenCV ecosystem that includes a feauture-rich open-source annotation tool With CVAT, you can annotate images and videos by creating classifications, segmentations, 3D cuboids, and skeleton templates Over 1 million people have downloaded it since CVAT launched, and under OpenCV, there’s an even larger community of users to ask for guidance and support.  Best for:  Data ops and annotation teams that need access to an open-source tool and ecosystem of ML engineers and annotators.  Pricing:  Free, open-source.  SuperAnnotate SuperAnnotate is a commercial platform and toolkit for creating annotations and labels, managing automated annotation workflows, and even generating images and datasets for computer vision projects. SuperAnnotate Benefits & key features:  SuperAnnotate includes a full-service Data Studio, including access to a marketplace of 400+ outsourced annotation teams and service providers  It also comes with an ML Studio to manage computer vision and AI-based workflows, including AI data management and curation, MLOps and automation, and quality assurance (QA) It’s designed for numerous use cases, including healthcare, insurance, sports, autonomous driving, and several others.  Best for:  ML engineers, data scientists, annotation teams, and MLOps professionals in academia, businesses, and enterprise organizations.  Pricing: Free for early-stage startups and academic researchers. You would need a demo or contact sales for the Pro and Enterprise plans.   Dataloop Dataloop is a "data engine for AI" that includes automated annotation for video datasets, full lifecycle dataset management, and AI-powered model training tools. Dataloop Benefits & key features:  Multiple data types supported, including numerous video file formats Automated and AI-powered data labeling End-to-end annotation and QA workflow managment and dashboards for collaborative working Best for:  ML, data ops, enterprise AI teams, and managing video annotation workflows with outsourced teams. Pricing:  From $85/mo for 150 annotation tool hours. Supervisely Supervisely is a "Unified OS enterprise-grade platform for computer vision" that includes video annotation tools and features. Supervisely Benefits & key features:  Native video file support, so that you don't need to cut them into segments or images Automated multi-track timelines within videos Built-in object tracking and segments tagging tools, and numerous other features for video annotation, QA, collaborative working, and computer vision model development Best for:  ML, data ops, and AI teams in Fortune 500 companies and computer vision research teams. Pricing:  30-day free trial, with custom plans after signing-up for a demo. Scale Scale is positioned as the AI data labeling and project/workflow management platform for “generative AI companies, US government agencies, enterprise organizations, and startups.”  Building the best AI, ML, and CV models means accessing the “best data,” and for that reason, it comes with tools and solutions such as the Scale Data Engine and Generative AI Platform.  Scale, an enterprise-grade data engine and generative AI platform Benefits & key features:  A Data Engine to unlock data organizations already have or can tap into vast public and open-source datasets  Tools to create synthetic data (e.g., generative AI features) A full-stack Generative AI platform for AI companies and US government agencies  An extensive developers platform for Large Language Model (LLM) applications.  Best for:  Data scientists and ML engineers in generative AI companies, US government agencies, enterprise organizations, and startups.  Pricing:  There are two core offerings: Label My Data (priced per-label), and an Enterprise plan that requires a demo to secure a price. Img Lab Img Lab is an open-source image annotation tool to “simplify image labeling/ annotation process with multiple supported formats.”  Img Lab Benefits & key features:  Img Lab isn’t as feature-rich as most of the tools and platforms on this list. It would need to be integrated with other tools and applications to ensure it could be used effectively for large-scale image annotation projects.  Best for:  Img Lab seems best equipped for annotators and those who need a quick and easy-to-use open-source annotation tool.  Pricing:  Free, open-source.   How To Pick the Best Video Annotation Tool for Computer Vision Projects? And there we go, the best video annotation tools for computer vision! In this post, we covered Encord, LabelMe, CVAT, SuperAnnotate, Dataloop, Supervisely, Scale, and Img Lab.  Each tool and suite of features that are included are applicable to a wide-range of use cases, data types, and project scales. Making the right choice depends on what your computer vision project needs, such as supporting various data modalities and annotation types, active learning strategies, and pricing.  When you’ve selected the best annotation tool for your project or AI application will accelerate model development, enhance the quality of your training data, and optimize your data labeling and annotation process.  {{Training_data_CTA}}

Data quality is paramount in data science and machine learning. The input data quality heavily influences machine learning models' performance. In this context, data cleaning and preprocessing are not just preliminary steps but crucial components of the machine learning pipeline. Data cleaning involves identifying and correcting errors in the dataset, such as dealing with missing or inconsistent data, removing duplicates, and handling outliers. Ensuring you train the machine learning mode on accurate and reliable data is essential. The model may learn from incorrect data without proper cleaning, leading to inaccurate predictions or classifications. On the other hand, data preprocessing is a broader concept that includes data cleaning and other steps to prepare the data for machine learning algorithms. These steps may include data transformation, feature selection, normalization, and reduction. The goal of data preprocessing is to convert raw data into a suitable format that machine learning algorithms can learn. The importance of data cleaning and data preprocessing cannot be overstated, as it can significantly impact the model's performance. A well-cleaned and preprocessed dataset can lead to more accurate and reliable machine learning models, while a poorly cleaned and preprocessed dataset can lead to misleading results and conclusions. This guide will delve into the techniques and best data cleaning and data preprocessing practices. You will learn their importance in machine learning, common techniques, and practical tips to improve your data science pipeline. Whether you are a beginner in data science or an experienced professional, this guide will provide valuable insights to enhance your data cleaning and preprocessing skills. {{product_sam_cta}} Data Cleaning What is Data Cleaning? In data science and machine learning, the quality of input data is paramount. It's a well-established fact that data quality heavily influences the performance of machine learning models. This makes data cleaning, detecting, and correcting (or removing) corrupt or inaccurate records from a dataset a critical step in the data science pipeline. Data cleaning is not just about erasing data or filling in missing values. It's a comprehensive process involving various techniques to transform raw data into a format suitable for analysis. These techniques include handling missing values, removing duplicates, data type conversion, and more. Each technique has its specific use case and is applied based on the data's nature and the analysis's requirements. Common Data Cleaning Techniques Handling Missing Values: Missing data can occur for various reasons, such as errors in data collection or transfer. There are several ways to handle missing data, depending on the nature and extent of the missing values. Imputation: Here, you replace missing values with substituted values. The substituted value could be a central tendency measure like mean, median, or mode for numerical data or the most frequent category for categorical data. More sophisticated imputation methods include regression imputation and multiple imputation. Deletion: You remove the instances with missing values from the dataset. While this method is straightforward, it can lead to loss of information, especially if the missing data is not random. Removing Duplicates: Duplicate entries can occur for various reasons, such as data entry errors or data merging. These duplicates can skew the data and lead to biased results. Techniques for removing duplicates involve identifying these redundant entries based on key attributes and eliminating them from the dataset. Data Type Conversion: Sometimes, the data may be in an inappropriate format for a particular analysis or model. For instance, a numerical attribute may be recorded as a string. In such cases, data type conversion, also known as datacasting, is used to change the data type of a particular attribute or set of attributes. This process involves converting the data into a suitable format that machine learning algorithms can easily process. Outlier Detection: Outliers are data points that significantly deviate from other observations. They can be caused by variability in the data or errors. Outlier detection techniques are used to identify these anomalies. These techniques include statistical methods, such as the Z-score or IQR method, and machine learning methods, such as clustering or anomaly detection algorithms. {{light_callout_start}} Interested in outlier detection? Read Top Tools for Outlier Detection in Computer Vision. {{light_callout_end}}  Data cleaning is a vital step in the data science pipeline. It ensures that the data used for analysis and modeling is accurate, consistent, and reliable, leading to more robust and reliable machine learning models. {{light_callout_start}} Remember, data cleaning is not a one-size-fits-all process. The techniques used will depend on the nature of the data and the specific requirements of the analysis or model.  {{light_callout_end}}  Data Preprocessing What is Data Preprocessing? Data preprocessing is critical in data science, particularly for machine learning applications. It involves preparing and cleaning the dataset to make it more suitable for machine learning algorithms. This process can reduce complexity, prevent overfitting, and improve the model's overall performance. The data preprocessing phase begins with understanding your dataset's nuances and the data's main issues through Exploratory Data Analysis. Real-world data often presents inconsistencies, typos, missing data, and different scales. You must address these issues to make the data more useful and understandable. This process of cleaning and solving most of the issues in the data is what we call the data preprocessing step. Skipping the data preprocessing step can affect the performance of your machine learning model and downstream tasks. Most models can't handle missing values, and some are affected by outliers, high dimensionality, and noisy data. By preprocessing the data, you make the dataset more complete and accurate, which is critical for making necessary adjustments in the data before feeding it into your machine learning model. Data preprocessing techniques include data cleaning, dimensionality reduction, feature engineering, sampling data, transformation, and handling imbalanced data. Each of these techniques has its own set of methods and approaches for handling specific issues in the data. Common Data Preprocessing Techniques Data Scaling Data scaling is a technique used to standardize the range of independent variables or features of data. It aims to standardize the data's range of features to prevent any feature from dominating the others, especially when dealing with large datasets. This is a crucial step in data preprocessing, particularly for algorithms sensitive to the range of the data, such as deep learning models. There are several ways to achieve data scaling, including Min-Max normalization and Standardization. Min-Max normalization scales the data within a fixed range (usually 0 to 1), while Standardization scales data with a mean of 0 and a standard deviation of 1. Encoding Categorical Variables Machine learning models require inputs to be numerical. If your data contains categorical data, you must encode them to numerical values before fitting and evaluating a model. This process, known as encoding categorical variables, is a common data preprocessing technique. One common method is One-Hot Encoding, which creates new binary columns for each category/label in the original columns. Data Splitting Data Splitting is a technique to divide the dataset into two or three sets, typically training, validation, and test sets. You use the training set to train the model and the validation set to tune the model's parameters. The test set provides an unbiased evaluation of the final model. This technique is essential when dealing with large data, as it ensures the model is not overfitted to a particular subset of data. {{light_callout_start}} For more details on data splitting, read Training, Validation, Test Split for Machine Learning Datasets. {{light_callout_end}} Handling Missing Values Missing data in the dataset can lead to misleading results. Therefore, it's essential to handle missing values appropriately. Techniques for handling missing values include deletion, removing the rows with missing values, and imputation, replacing the missing values with statistical measures like mean, median, or model. This step is crucial in ensuring the quality of data used for training machine learning models. Feature Selection Feature selection is a process in machine learning where you automatically select those features in your data that contribute most to the prediction variable or output in which you are interested. Having irrelevant features in your data can decrease the accuracy of many models, especially linear algorithms like linear and logistic regression. This process is particularly important for data scientists working with high-dimensional data, as it reduces overfitting, improves accuracy, and reduces training time. Three benefits of performing feature selection before modeling your data are: Reduces Overfitting: Less redundant data means less opportunity to make noise-based decisions. Improves Accuracy: Less misleading data means modeling accuracy improves. Reduces Training Time: Fewer data points reduce algorithm complexity, and it trains faster. Data Cleaning Process Data cleaning, a key component of data preprocessing, involves removing or correcting irrelevant, incomplete, or inaccurate data. This process is essential because the quality of the data used in machine learning significantly impacts the performance of the models. Step-by-Step Guide to Data Cleaning Following these steps ensures your data is clean, reliable, and ready for further preprocessing steps and eventual analysis. Identifying and Removing Duplicate or Irrelevant Data: Duplicate data can arise from various sources, such as the same individual participating in a survey multiple times or redundant fields in the data collection process. Irrelevant data refers to information you can safely remove because it is not likely to contribute to the model's predictive capacity. This step is particularly important when dealing with large datasets. Fixing Syntax Errors: Syntax errors can occur due to inconsistencies in data entry, such as date formats, spelling mistakes, or grammatical errors. You must identify and correct these errors to ensure the data's consistency. This step is crucial in maintaining the quality of data. Filtering out Unwanted Outliers: Outliers, or data points that significantly deviate from the rest of the data, can distort the model's learning process. These outliers must be identified and handled appropriately by removal or statistical treatment. This process is a part of data reduction. Handling Missing Data: Missing data is a common issue in data collection. Depending on the extent and nature of the missing data, you can employ different strategies, including dropping the data points or imputing missing values. This step is especially important when dealing with large data. Validating Data Accuracy: Validate the accuracy of the data through cross-checks and other verification methods. Ensuring data accuracy is crucial for maintaining the reliability of the machine-learning model. This step is particularly important for data scientists as it directly impacts the model's performance. Best Practices for Data Cleaning Here are some practical tips and best practices for data cleaning: Maintain a strict data quality measure while importing new data. Use efficient and accurate algorithms to fix typos and fill in missing regions. Validate data accuracy with known factors and cross-checks. Remember that data cleaning is not a one-time process but a continuous one. As new data comes in, it should also be cleaned and preprocessed before being used in the model. By following these practices, we can ensure that our data is clean and structured to maximize the performance of our machine-learning models. Tools and Libraries for Data Cleaning Various tools and libraries have been developed to aid this process, each with unique features and advantages. One of the most popular libraries for data cleaning is Pandas in Python. This library provides robust data structures and functions for handling and manipulating structured data. It offers a wide range of functionalities for data cleaning, including handling missing values, removing duplicates, and standardizing data. For instance, Pandas provides functions such as `dropna()` for removing missing values and `drop_duplicates()` for removing duplicate entries. It also offers functions like quantile() for handling outliers and MinMaxScaler() and StandardScaler() for data standardization. {{light_callout_start}} The key to effective data cleaning is understanding your data and its specific cleaning needs. Tools like Pandas provide a wide range of functionalities, but applying them effectively is up to you. {{light_callout_end}} Another useful tool for data cleaning is the DataHeroes library, which provides a CoresetTreeServiceLG class optimized for data cleaning. This tool computes an "Importance" metric for each data sample, which can help identify outliers and fix mislabeling errors, thus validating the dataset. The FuzzyWuzzy library in Python can be used for fuzzy matching to identify and remove duplicates that may not be exact matches due to variations in data entry or formatting inconsistencies. Real-World Applications of Data Cleaning and Data Preprocessing Data cleaning and data preprocessing in data science are theoretical concepts and practical necessities. They play a pivotal role in enhancing the performance of machine learning models across various industries. Let's delve into some real-world examples that underscore their significance. Improving Customer Segmentation in Retail One of the most common data cleaning and preprocessing applications is in the retail industry, particularly in customer segmentation. Retailers often deal with vast amounts of customer data, which can be messy and unstructured. They can ensure the data's quality by employing data-cleaning techniques such as handling missing values, removing duplicates, and correcting inconsistencies. When preprocessed through techniques like normalization and encoding, this cleaned data can significantly enhance the performance of machine learning models for customer segmentation, leading to more accurate targeting and personalized marketing strategies. Enhancing Predictive Maintenance in Manufacturing The manufacturing sector also benefits immensely from data cleaning and data preprocessing. For instance, machine learning models predict equipment failures in predictive maintenance. However, the sensor data collected can be noisy and contain outliers. One can improve the data quality by applying data cleaning techniques to remove these outliers and fill in missing values. Further, preprocessing steps like feature scaling can help create more accurate predictive models, reducing downtime and saving costs. Streamlining Fraud Detection in Finance Data cleaning and preprocessing are crucial for fraud detection in the financial sector. Financial transaction data is often large and complex, with many variables. Cleaning this data by handling missing values and inconsistencies, and preprocessing it through techniques like feature selection, can significantly improve the performance of machine learning models for detecting fraudulent transactions. These examples highlight the transformative power of data cleaning and data preprocessing in various industries. By ensuring data quality and preparing it for machine learning models, these processes can lead to more accurate predictions and better decision-making. Data Cleaning & Data Preprocessing: Key Takeaways Data cleaning and preprocessing are foundational steps ensuring our models' reliability and accuracy, safeguarding them from misleading data and inaccurate predictions. This comprehensive guide explored various data cleaning and preprocessing techniques and tools—the importance of these processes and how they impact the overall data science pipeline. We've explored techniques like handling missing values, removing duplicates, data scaling, and feature selection, each crucial role in preparing your data for machine learning models. We've also delved into the realm of tools and libraries that aid in these processes, such as the versatile Pandas library and the specialized DataHeroes library. When used effectively, these tools can significantly streamline data cleaning and preprocessing tasks. Remember that every dataset is unique, with its challenges and requirements. Therefore, the real test of your data cleaning skills lies in applying these techniques to your projects, tweaking and adjusting as necessary to suit your needs. {{Training_data_CTA}}