Artificial intelligence (AI) is solving some of society's most critical issues. But, lack of transparency and visibility in building models make AI a black box. Users cannot understand what goes on behind the scenes when AI answers a question, gives a prediction, or makes a critical decision. What happens when large language models (LLMs), like GPT-4 and LLaMA, make errors, but users fail to realize the mistake? Or how badly is the model’s credibility affected when users identify errors in the model outcome?  Customers lose trust, and the company behind the model can face serious legal and financial consequences. Remember how a minor factual error in Bard’s demo reportedly cost Google $100 billion in market value?  That’s where model observability comes into play. It helps understand how a model reaches an outcome using different techniques. In this article, you will: Understand model observability and the importance Challenges related to model observability How model observability applies to modern AI domains, like computer vision and natural language processing (NLP)  What is Model Observability? Model observability is a practice to validate and monitor machine learning (ML) model performance and behavior by measuring critical metrics, indicators, and processes to ensure that the model works as expected in production.  It involves end-to-end event logging and tracing to track and diagnose issues quickly during training, inference, and decision-making cycles. It lets you monitor and validate training data to check if it meets the required quality standards. It also assists in model profiling, detecting bias and anomalies that could affect the ML model's performance Through observability, ML engineers can conduct root-cause analysis to understand the reason behind a particular issue. This practice allows for continuous performance improvement using a streamlined ML workflow that enables scalability and reduces time to resolution. A significant component of observability is model explainability, which operates under explainable AI or XAI. XAI facilitates root-cause analysis by enabling you to investigate a model’s decision-making process. XAI optimizes model development, debugging, and testing cycles using tools and frameworks. ML model observability is often used interchangeably with ML monitoring. However, an emphasis on model explainability and a focus on why a specific issue occurred makes ML observability broader than ML monitoring. While model monitoring only tells you where and what the problem is, observability goes further by helping you understand the primary reason behind a particular situation. Learn more about the difference between model monitoring and model observability by reading our detailed article ML Observability vs. ML Monitoring   Significance of Model Observability ML models in production can encounter several issues that can cause performance degradation and result in poor user experience. These issues can go unnoticed if model observability practices are not followed. With a robust model observability pipeline, data scientists can prevent such problems and speed up the development lifecycle. Below are a few factors that can go wrong during production and warrant the need for model observability. Data Drift Data drift occurs when the statistical properties of a machine learning model’s training data change. It can be a covariate shift where input feature distributions change or a model drift where the relationship between the input and target variables becomes invalid. The divergence between the real-world and training data distribution can occur for multiple reasons, such as changes in underlying customer behavior, changes in the external environment, demographic shifts, product upgrades, etc. Data Drift Performance Degradation As the machine learning application attracts more users, model performance can deteriorate over time due to model overfitting, outliers, adversarial attacks, and changing data patterns. Data Quality Ensuring consistent data quality during production is challenging as it relies heavily on data collection methods, pipelines, storage platforms, pre-processing techniques, etc. Problems such as missing data, labeling errors, disparate data sources, privacy restraints, inconsistent formatting, and lack of representativeness can severely damage data quality and cause significant prediction errors. Let’s discuss how model observability helps. Faster Detection and Resolution of Issues Model observability solutions track various data and performance metrics in real-time to quickly notify teams if particular metrics breach thresholds and enhance model interpretability to fix the root cause of the problem. Regulatory Compliance  Since observability tools maintain logs of model behavior, datasets, performance, predictions, etc., they help with compliance audits and ensure that the model development process aligns with regulatory guidelines. Moreover, they help detect bias by making the model decision-making process transparent through XAI. Fostering Customer Trust Model observability enables you to build unbiased models with consistent behavior and reduces major model failures. Customers begin to trust applications based on these models and become more willing to provide honest feedback, allowing for further optimization opportunities. Now, let’s discuss how model observability improves the ML pipeline for different domains. Model Observability in Large Language Models (LLMs) and Computer Vision (CV) Standard machine learning observability involves model validation, monitoring, root-cause analysis, and improvement. During validation, an observability platform evaluates model performance on unseen test datasets and assesses whether a model suffers from bias or variance. It helps you detect issues during production through automated metrics. Finally, insights from the root-cause analysis help you improve the model and prevent similar problems from occurring again. While the flow above applies to all ML models, monitoring and evaluation methods can differ according to model complexity. For instance, LLMs and CV models process unstructured data and have complex inference procedures. So, model observability requires advanced explainability, evaluation, and monitoring techniques to ensure that the models perform as expected. Let’s discuss the main challenges of such models and understand the critical components required to make model observability effective in these domains. Model Observability in Large Language Models LLMs can suffer from unique issues, as discussed below. Hallucinations: Occurs when LLMs generate non-sensical or inaccurate responses to user prompts. No single ground truth: A significant challenge in evaluating an LLM's response is the absence of a single ground truth. LLMs can generate multiple plausible answers, and assessing which is accurate is problematic. Response quality: While responses may be factually accurate, they may not be relevant to a user’s prompt. Also, the language may be ambiguous with an inappropriate tone. The challenge here is to monitor response and prompt quality together, as a poorly crafted prompt will generate sub-optimal responses. Jailbreaks: Specific prompts can cause LLMs to disregard security protocols and generate harmful, biased, and offensive responses. Cost of retraining: Retraining LLMs is necessary to ensure that they generate relevant responses using the latest information. However, retraining is costly. It demands a robust infrastructure involving advanced hardware, personnel expenses, data management costs, etc. You can mitigate the above challenges using a tailored model observability strategy that will allow for better evaluation techniques. Below are common techniques to evaluate LLMs. User feedback: Users can quickly identify and report problems, such as bias, misinformation, and unethical LLM responses to specific prompts. Collecting and assessing user feedback can help improve response quality.  Embedding visualization: Comparing the model response and input prompt embeddings in a semantic space can reveal how close the responses to particular prompts are. The method can help evaluate response relevance and accuracy. A Word Embedding Plot Showcasing Relevance of Similar Words Prompt engineering: You can enhance LLM performance by investigating how it responds to several prompt types. It can also help reveal jailbreaking prompts early in the development process. Retrieval systems: A retrieval system can help you assess whether an LLM fetches the correct information from relevant sources. You can experiment by feeding different data sources into the model and trying various user queries to see if the LLM retrieves relevant content. Fine-tuning: Instead of re-training the entire LLM from scratch, you can fine-tune the model on domain-specific input data. Model Observability in Computer Vision Similar to LLMs, CV models have specific issues that require adapting model observability techniques for effectiveness. Below are a few problems with CV modeling. Image drift: Image data drift occurs when image properties change over time. For instance, certain images may have poor lighting, different background environments, different camera angles, etc. Occlusion: Occlusion happens when another object blocks or hides an image's primary object of interest. It causes object detection models to classify objects wrongly and reduces model performance. Occlusion Lack of annotated samples: CV models often require labeled images for training. However, finding sufficient domain-specific images with correct labels is challenging. Labeling platforms like Encord can help you mitigate these issues. Sensitive use cases: CV models usually operate in safety-critical applications like medical diagnosis and self-driving cars. Minor errors can lead to disastrous consequences. As such, model observability in CV must have the following components to address the problems mentioned above. Monitoring metrics: Use appropriate metrics to measure image quality and model performance. Specialized workforce: Domain experts must be a part of the model observability pipeline to help annotators with the image labeling process. Quality of edge devices: Image data collection through edge devices, such as remote cameras, drones, sensors, etc., requires real-time monitoring methods to track a device’s health, bandwidth, latency, etc. Label quality: Ensuring high label quality is essential for effective model training. Automation in the labeling process with a regular review system can help achieve quality standards. Smart labeling techniques, such as active, zero-shot, and few-shot learning, should be part of the observability framework to mitigate annotation challenges. Domain adaptation: CV observability should help indicate when fine-tuning a CV model is suitable by measuring the divergence between source and target data distribution. It should also help inform the appropriate adaptation technique. Want to learn how active learning can solve your CV data annotation challenges? Read our detailed article A Practical Guide to Active Learning for Computer Vision. Now, let’s explore the two crucial aspects of model observability: monitoring and explainability. We’ll discuss various techniques that you can employ in your ML pipelines. Model Monitoring Techniques The following model metrics are commonly used for evaluating the performance of standard ML systems, LLMs, and CV models. Standard ML monitoring metrics: Precision, recall, F1 score, AUC ROC, and Mean Absolute Error (MAE) are common techniques for assessing model performance by evaluating true positives and negatives against false positives and negatives. LLM monitoring metrics: Automated scores like BLEU, ROUGE, METEOR, and CiDER help measure the model response quality by matching n-grams in candidate and target texts. Moreover, human-level monitoring is preferable in generative AI domains due to the absence of a single ground truth. User feedback, custom metrics, and manually evaluating response relevance are a few ways to conduct human-based assessments. You can also implement reinforcement learning with human feedback (RLHF) to ensure LLM output aligns with human preferences. CV monitoring metrics: Metrics for monitoring CV models include mean average precision (mAP), intersection-over-union (IoU), panoptic quality (PQ), etc., for various tasks, such as object detection, image classification, and segmentation. Model Explainability Techniques The primary difference between ML observability and monitoring is that the former encompasses a variety of explainability techniques (XAI) to help you understand the fundamental reason behind an anomaly or error. XAI techniques can be classified into three levels of explainability:  Global,  Cohort,  Local.  Global explainability tells you which feature has the most significant contribution across all predictions on average. Global explainability helps people without a data science domain to interpret the model reasoning process.  For instance, the diagram below shows a model for predicting credit limits for individuals relies heavily on age. This behavior can be problematic since the model will always predict a lower credit limit for younger individuals, despite their ability to pay more. Global Explainability for a Model’s Features Similarly, cohort explainability techniques reveal which features are essential for predicting outputs on a validation or test dataset. A model may assign importance to one feature in the training phase but use another during validation. You can use cohort explainability to identify these differences and understand model behavior more deeply. The illustration below suggests the model uses “charges” as the primary feature for predicting credit limits for people under 30. Finally, local explainability methods allow you to see which feature the model used for making a particular prediction in a specific context during production. For instance, the credit limit model may predict a lower credit limit for someone over 50. Analyzing this case may reveal that the model used account balance as the primary predictor instead of age. Such a level of detail can help diagnose an issue more efficiently and indicate the necessary improvements. Explainability Techniques in Standard ML Systems Let’s briefly discuss the two techniques you can use to interpret a model’s decision-making process.  SHAP: Shapley Additive Explanations (SHAP) computes the Shapley value of each feature, indicating feature importance for global and local explainability. LIME: Local Interpretable Model-Agnostic Explanations (LIME) perturbs input data to generate fake predictions. It then trains a simpler model on the generated values to measure feature importance. Explainability Techniques in LLMs LLM explainability is challenging since the models have complex deep-learning architectures with many parameters. However, the explainability techniques mentioned below can be helpful. Attention-based techniques: Modern LLMs, such as ChatGPT, BERT, T5, etc., use the transformer architecture, which relies on the attention mechanism to perform NLP tasks. The attention-based explainability technique considers multiple words and relates them to their context for predictions. With attention visualization, you can see which words in an input sequence the model considered the most for predictions. Saliency-based techniques: Saliency methods consist of computing output gradients with respect to input features for measuring importance. The intuition is that features with high gradient values are essential for predictions. Similarly, you can erase or mask a particular input feature and analyze output variation. High variations mean that the feature is important. Explainability Techniques in CV While saliency-based methods are applicable for explaining CV model predictions, other methods specific to CV tasks can do a better job, such as: Integrated gradients The idea behind integrated gradients (IG) is to build a baseline image that contains no relevant information about an object. For instance, a baseline image can only have random noise. You can gradually add features, or pixels to the image and compute gradients at each stage. Adding up the gradients for each feature along the path will reveal the most important features for predicting the object within the image. IG Showing Which Pixels Are Important For Predicting the Image of a Cockatoo XRAI XRAIenhances the IG approach by highlighting pixel regions instead of single pixels by segmenting similar image patches and then computing the saliency of each region through gradients. XRAI Showing the Important Regions for Prediction Grad-CAM Gradient-weighted Class Activation Mapping (Grad-CAM) generates a heatmap for models based on Convolutional Neural Nets (CNNs). The heatmap highlights the regions of importance by overlaying it on the original image. Grad-CAM’s Heatmap Model Observability with Encord Active Encord Active is an evaluation platform for computer vision models. It helps you understand and improve your data, labels, and models at all stages of your computer vision journey. Here are some observability workflows you can use Encord Active to execute: Understanding Model Failure Using Detailed Explainability Reports Within Encord Active, you can visualize the impact of data and label quality metrics on your model's performance. This can also visualize Precision or Recall by Metric based on the model results to know which instances it needs to predict better. Let’s see how you can achieve this step-by-step within Encord Active. Make sure you have a project selected and have successfully imported model predictions (through an API or the UI).   Step 1: Navigate to the Model Evaluation tab. Step 2: Toggle the Metric Performance button. You will see a graph of your model’s performance based on the precision by the quality metric you select to identify model failure modes. Debug Data Annotation Errors When developing ML systems or running them in production, one key issue to look out for is the quality of your annotations. They can make or break the ML systems because they are only as good as the ground truth they are trained on. Here’s how you could do it within Active Cloud: Step 1: Navigate to Explorer and Click Predictions Step 2: Click Add Filter >> Annotation Quality Metrics, and select a metric you want to inspect. Perform Root-Cause Analysis Encord Active provides a quick method to filter unwanted or problematic images by auto-discovering the issues in your model predictions. Here’s how to do it: Ensure you are on the Explorer tab. Click on Predictions and on the right hand side of your scream, you should see model prediction issues Active has surfaced including the types. Those are shortcuts to improving datasets and model performance. They give you a quick launch pad to improve your data and model performance. With Encord Active, you can also: Use automated CV pipelines to build robust models. Use pre-built or custom quality metrics to assess CV model performance. Run robustness checks on your model before and after deployment. Challenges of Model Observability Despite effective tools, implementing an end-to-end model observability pipeline takes time and effort. The list below highlights the most pressing issues you must address before building an efficient observability workflow. Increasing model complexity: Modern AI models are highly complex, with many integrated modules, deep neural nets, pre-processing layers, etc. Validating, monitoring, and performing root-cause analysis on these sophisticated architectures is tedious and time-consuming. XAI in multimodal models: Explaining models that use multiple modalities - text, image, sound, etc. - is difficult. For instance, using salience-based methods to explain vision-language models that combine NLP and visual information for inferencing would be incredibly costly due to the sheer number of parameters. Privacy concerns: Privacy restrictions mean you cannot freely analyze model performance on sensitive data, which may lead to compliance issues. Scalability: Observing models that receive considerable inference requests requires a robust infrastructure to manage large data volumes. Human understanding and bias: This challenge concerns XAI, where explanations can differ based on the model used. Also, understanding explainability results requires background knowledge that teams outside of data science may not have. Future Trends in Model Observability While the above challenges make optimal model observability difficult to achieve, research in the direction of XAI shows promising future trends. For instance, there is considerable focus on making XAI more user-friendly by building techniques that generate simple explanations. Also, research into AI model fairness is ongoing, emphasizing using XAI to visualize learned features and employing other methods to detect bias. In addition, studies that combine insights from other disciplines, such as psychology and philosophy, are attempting to find more human-centric explainability methods. Finally, causal AI is a novel research area that aims to find techniques to highlight why a model uses a particular feature for predictions. This method can add value to explanations and increase model robustness. Model Observability: Key Takeaways As AI becomes more complex, observability will be crucial for optimizing model performance. Below are a few critical points you must remember about model observability. Observability for optimality: Model observability is essential for optimizing modern AI models through efficient validation, monitoring, and root-cause analysis. Better productivity: Observability improves resource allocations, reduces the cost of re-training, builds customer trust, ensures regulatory compliance, and speeds up error detection and resolution. LLM and CV observability: Observability for LLMs and CV models differs slightly from standard ML observability due to issues specific to NLP and CV tasks. Monitoring and explainability: Model monitoring and explainability are two primary components of observability, each with different techniques. Challenges and future trends: Model complexity is the primary issue with observability and will require better explainability techniques to help users understand how modern models think.

Update: Segment Anything (SAM) is live in Encord! Check out our tutorial on How To Fine-Tune Segment Anything or book a demo with the team to learn how to use the Segment Anything Model (SAM) to reduce labeling costs. If you thought the AI space was already moving fast with ChatGPT, GPT4, and Stable Diffusion, then strap in and prepare for the next groundbreaking innovation in AI. Meta’s FAIR lab has just released the Segment Anything Model (SAM), a state-of-the-art image segmentation model that aims to change the field of computer vision.  SAM is based on foundation models that have significantly impacted natural language processing (NLP). It focuses on promptable segmentation tasks, using prompt engineering to adapt to diverse downstream segmentation problems. In this blog post, you will:  Learn how it compares to other foundation models. Learn about the Segment Anything (SA) project Dive into SAM's network architecture, design, and implementation. Learn how to implement and fine-tune SAM Discover potential uses of SAM for AI-assisted labeling. Why Are We So Excited About SAM?  Having tested it out for a day now, we can see the following incredible advances: SAM can segment objects by simply clicking or interactively selecting points to include or exclude from the object. You can also create segmentations by drawing bounding boxes or segmenting regions with a polygon tool, and it will snap to the object. When encountering uncertainty in identifying the object to be segmented, SAM can produce multiple valid masks. SAM can identify and generate masks for all objects present in an image automatically. After precomputing the image embeddings, SAM can provide a segmentation mask for any prompt instantly for real-time interaction with the model. A Brief History of Meta's AI & Computer Vision As one of the leading companies in the field of artificial intelligence (AI), Meta has been pushing the boundaries of what's possible with machine learning models: from recently released open source models such as LLaMA to developing the most used Python library for ML and AI, PyTorch. Advances in Computer Vision Computer vision has also experienced considerable advancements, with models like CLIP bridging the gap between text and image understanding. These models use contrastive learning to map text and image data. This allows them to generalize to new visual concepts and data distributions through prompt engineering.  FAIR’s Segment Anything Model (SAM) is the latest breakthrough in this field. Their goal was to create a foundation model for image segmentation that can adapt to various downstream tasks using prompt engineering. The release of SAM started a wave of vision foundation models and vision language models like LLaVA, GPT-4 vision, Gemini, and many more. Let’s briefly explore some key developments in computer vision that have contributed to the growth of AI systems like Meta's. Convolutional Neural Networks (CNNs) CNNs, first introduced by Yann LeCun (now VP & Chief AI Scientist at Meta) in 1989, have emerged as the backbone of modern computer vision systems, enabling machines to learn and recognize complex patterns in images automatically.  By employing convolutional layers, CNNs can capture local and global features in images, allowing them to recognize objects, scenes, and actions effectively. This has significantly improved tasks such as image classification, object detection, and semantic segmentation. Generative Adversarial Networks (GANs) GANs are a type of deep learning model that Ian Goodfellow and his team came up with in 2014. They are made up of two neural networks, a generator, and a discriminator, competing. The generator aims to create realistic outputs, while the discriminator tries to distinguish between real and generated outputs. The competition between these networks has resulted in the creation of increasingly realistic synthetic images. It has led to advances in tasks such as image synthesis, data augmentation, and style transfer. Transfer Learning and Pre-trained Models Like NLP, computer vision has benefited from the development of pre-trained models that can be fine-tuned for specific tasks. Models such as ResNet, VGG, and EfficientNet have been trained on large-scale image datasets, allowing researchers to use these models as a starting point for their own projects. The Growth of Foundation Models Foundation models in natural language processing (NLP) have made significant strides in recent years, with models like Meta’s own LLaMA or OpenAI’s GPT-4 demonstrating remarkable capabilities in zero-shot and few-shot learning.  These models are pre-trained on vast amounts of data and can generalize to new tasks and data distributions by using prompt engineering. Meta AI has been instrumental in advancing this field, fostering research and the development of large-scale NLP models that have a wide range of applications. Here, we explore the factors contributing to the growth of foundation models. Large-scale Language Models The advent of large-scale language models like GPT-4 has been a driving force behind the development of foundation models in NLP. These models employ deep learning architectures with billions of parameters to capture complex patterns and structures in the training data.  Transfer Learning A key feature of foundation models in NLP is their capacity for transfer learning. Once trained on a large corpus of data, they can be fine-tuned on smaller, task-specific datasets to achieve state-of-the-art performance across a variety of tasks. Zero-shot and Few-shot Learning Foundation models have also shown promise in zero-shot and few-shot learning, where they can perform tasks without fine-tuning or with minimal task-specific training data. This capability is largely attributed to the models' ability to understand and generate human-like responses based on the context provided by prompts. Multi-modal Learning Another growing area of interest is multi-modal learning, where foundation models are trained to understand and generate content across different modalities, such as text and images.  Models like Gemini, GPT-4V, LLaVA, CLIP, and ALIGN show how NLP and computer vision could be used together to make multi-modal models that can translate actions from one domain to another. Read the blog Google Launches Gemini, Its New Multimodal AI Model to learn about the recent advancements in multimodal learning. Ethical Considerations and Safety The growth of foundation models in NLP has also raised concerns about their ethical implications and safety. Researchers are actively exploring ways to mitigate potential biases, address content generation concerns, and develop safe and controllable AI systems. Proof of this was the recent call for a six-month halt on all development of cutting-edge models. Segment Anything Model Vs. Previous Foundation Models SAM is a big step forward for AI because it builds on the foundations that were set by earlier models. SAM can take input prompts from other systems, such as, in the future, taking a user's gaze from an AR/VR headset to select an object, using the output masks for video editing, abstracting 2D objects into 3D models, and even popular Google Photos tasks like creating collages. It can handle tricky situations by generating multiple valid masks where the prompt is unclear. Take, for instance, a user’s prompt for finding Waldo: Image displaying semantic segmentations by the Segment Anything Model (SAM) One of the reasons the results from SAM are groundbreaking is because of how good the segmentation masks are compared to other techniques like ViTDet. The illustration below shows a comparison of both techniques: Segmentation masks by humans, ViTDet, and the Segment Anything Model (SAM) Read the Segment Anything research paper for a detailed comparison of both techniques. What is the Segment Anything Model (SAM)? SAM, as a vision foundation model, specializes in image segmentation, allowing it to accurately locate either specific objects or all objects within an image. SAM was purposefully designed to excel in promptable segmentation tasks, enabling it to produce accurate segmentation masks based on various prompts, including spatial or textual clues that identify specific objects. Is Segment Anything Model Open Source? The short answer is YES! The SA-1B Dataset has been released as open source for research purposes. In addition, Meta AI released the pre-trained models (~2.4 GB in size) and code under Apache 2.0 (a permissive license) following FAIR’s commitment to open research. It is freely accessible on GitHub. The training dataset is also available, alongside an interactive demo web UI. FAIR Segment Anything (SA) Paper How does the Segment Anything Model (SAM) work? SAM's architectural design allows it to adjust to new image distributions and tasks seamlessly, even without prior knowledge, a capability referred to as zero-shot transfer. Utilizing the extensive SA-1B dataset, comprising over 11 million meticulously curated images with more than 1 billion masks, SAM has demonstrated remarkable zero-shot performance, often surpassing previous fully supervised results. Before we dive into SAM’s network design, let’s discuss the SA project, which led to the introduction of the very first vision foundation model. Segment Anything Project Components The Segment Anything (SA) project is a comprehensive framework for image segmentation featuring a task, model, and dataset.  The SA project starts with defining a promptable segmentation task with broad applicability, serving as a robust pretraining objective, and facilitating various downstream applications. This task necessitates a model capable of flexible prompting and the real-time generation of segmentation masks to enable interactive usage. Training the model effectively required a diverse, large-scale dataset. The SA project implements a "data engine" approach to address the lack of web-scale data for segmentation. This involves iteratively using our efficient model to aid in data collection and utilizing the newly gathered data to enhance the model's performance. Let’s look at the individual components of the SA project. Segment Anything Task  The Segment Anything Task draws inspiration from natural language processing (NLP) techniques, particularly the next token prediction task, to develop a foundational model for segmentation. This task introduces the concept of promptable segmentation, wherein a prompt can contain various ,input forms such as foreground/background points, bounding boxes, masks, or free-form text, indicating what objects to segment in an image. The objective of this task is to generate valid segmentation masks based on any given prompt, even in ambiguous scenarios. This approach enables a natural pre-training algorithm and facilitates zero-shot transfer to downstream segmentation tasks by engineering appropriate prompts.  Using prompts and composition techniques allows for extensible model usage across a wide range of applications, distinguishing it from interactive segmentation models designed primarily for human interaction. Dive into SAM's Network Architecture and Design SAM’s design hinges on three main components: The promptable segmentation task to enable zero-shot generalization. The model architecture. The dataset that powers the task and model. Leveraging concepts from Transformer vision models, SAM prioritizes real-time performance while maintaining scalability and powerful pretraining methods. Scroll down to learn more about SAM’s network design. Foundation model architecture for the Segment Anything (SA) model Task SAM was trained on millions of images and over a billion masks to return a valid segmentation mask for any prompt. In this case, the prompt is the segmentation task and can be foreground/background points, a rough box or mask, clicks, text, or, in general, any information indicating what to segment in an image. The task is also used as the pre-training objective for the model. Model SAM’s architecture comprises three components that work together to return a valid segmentation mask: An image encoder to generate one-time image embeddings. A prompt encoder that embeds the prompts. A lightweight mask decoder that combines the embeddings from the prompt and image encoders. Components of the Segment Anything (SA) model We will dive into the architecture in the next section, but for now, let’s take a look at the data engine. Segment Anything Data Engine The Segment Anything Data Engine was developed to address the scarcity of segmentation masks on the internet and facilitate the creation of the extensive SA-1B dataset containing over 1.1 billion masks. A data engine is needed to power the tasks and improve the dataset and model. The data engine has three stages: Model-assisted manual annotation, where professional annotators use a browser-based interactive segmentation tool powered by SAM to label masks with foreground/background points. As the model improves, the annotation process becomes more efficient, with the average time per mask decreasing significantly. Semi-automatic, where SAM can automatically generate masks for a subset of objects by prompting it with likely object locations, and annotators focus on annotating the remaining objects, helping increase mask diversity. The focus shifts to increasing mask diversity by detecting confident masks and asking annotators to annotate additional objects. This stage contributes significantly to the dataset, enhancing the model's segmentation capabilities. Fully automatic, where human annotators prompt SAM with a regular grid of foreground points, yielding on average 100 high-quality masks per image. The annotation becomes fully automated, leveraging model enhancements and techniques like ambiguity-aware predictions and non-maximal suppression to generate high-quality masks at scale. This approach enables the creation of the SA-1B dataset, consisting of 1.1 billion high-quality masks derived from 11 million images, paving the way for advanced research in computer vision. Segment Anything 1-Billion Mask Dataset The Segment Anything 1 Billion Mask (SA-1B) dataset is the largest labeled segmentation dataset to date. It is specifically designed for the development and evaluation of advanced segmentation models. We think the dataset will be an important part of training and fine-tuning future general-purpose models. This would allow them to achieve remarkable performance across diverse segmentation tasks. For now, the dataset is only available under a permissive license for research. The SA-1B dataset is unique due to its: Diversity Size High-quality annotations Diversity The dataset is carefully curated to cover a wide range of domains, objects, and scenarios, ensuring that the model can generalize well to different tasks. It includes images from various sources, such as natural scenes, urban environments, medical imagery, satellite images, and more.  This diversity helps the model learn to segment objects and scenes with varying complexity, scale, and context. Distribution of images and masks for training Segment Anything (SA) model Size The SA-1B dataset, which contains over a billion high-quality annotated images, provides ample training data for the model. The sheer volume of data helps the model learn complex patterns and representations, enabling it to achieve state-of-the-art performance on different segmentation tasks. Relative size of SA-1B to train the Segment Anything (SA) model High-Quality Annotations The dataset has been carefully annotated with high-quality masks, leading to more accurate and detailed segmentation results. In the Responsible AI (RAI) analysis of the SA-1B dataset, potential fairness concerns and biases in geographic and income distribution were investigated. The research paper showed that SA-1B has a substantially higher percentage of images from Europe, Asia, and Oceania, as well as middle-income countries, compared to other open-source datasets. It's important to note that the SA-1B dataset features at least 28 million masks for all regions, including Africa. This is 10 times more than any previous dataset's total number of masks. Distribution of the images to train the Segment Anything (SA) model At Encord, we think the SA-1B dataset will enter the computer vision hall of fame (together with famous datasets such as COCO, ImageNet, and MNIST) as a resource for developing future computer vision segmentation models. Notably, the dataset includes downscaled images to mitigate accessibility and storage challenges while maintaining significantly higher resolution than many existing vision datasets. The quality of the segmentation masks is rigorously evaluated, with automatic masks deemed high quality and effective for training models, leading to the decision to include automatically generated masks exclusively in SA-1B. Let's delve into the SAM's network architecture, as it's truly fascinating. Segment Anything Model's Network Architecture The Segment Anything Model (SAM) network architecture contains three crucial components: the Image Encoder, the Prompt Encoder, and the Mask Decoder. Architecture of the Segment Anything (SA) universal segmentation model Image Encoder At the highest level, an image encoder (a masked auto-encoder, MAE, pre-trained Vision Transformer, ViT) generates one-time image embeddings. It is applied before prompting the model. The image encoder, based on a masked autoencoder (MAE) pre-trained Vision Transformer (ViT), processes high-resolution inputs efficiently. This encoder runs once per image and can be applied before prompting the model for seamless integration into the segmentation process. Prompt Encoder The prompt encoder encodes background points, masks, bounding boxes, or texts into an embedding vector in real-time. The research considers two sets of prompts: sparse (points, boxes, text) and dense (masks).  Points and boxes are represented by positional encodings and added with learned embeddings for each prompt type. Free-form text prompts are represented with an off-the-shelf text encoder from CLIP. Dense prompts, like masks, are embedded with convolutions and summed element-wise with the image embedding. The prompt encoder manages both sparse and dense prompts. Dense prompts, like masks, are embedded using convolutions and then summed element-wise with the image embedding. Sparse prompts, representing points, boxes, and text, are encoded using positional embeddings for each prompt type. Free-form text is handled with a pre-existing text encoder from CLIP. This approach ensures that SAM can effectively interpret various types of prompts, enhancing its adaptability. Mask Decoder A lightweight mask decoder predicts the segmentation masks based on the embeddings from both the image and prompt encoders. The mask decoder efficiently maps the image and prompt embeddings to generate segmentation masks. It maps the image embedding, prompt embeddings, and an output token to a mask. SAM uses a modified decoder block and then a dynamic mask prediction head, taking inspiration from already existing Transformer decoder blocks. This design incorporates prompt self-attention and cross-attention mechanisms to update all embeddings effectively. All of the embeddings are updated by the decoder block, which uses prompt self-attention and cross-attention in two directions (from prompt to image embedding and back). SAM's Mask Decoder is optimized for real-time performance, enabling seamless interactive prompting of the model. The masks are annotated and used to update the model weights. This layout enhances the dataset and allows the model to learn and improve over time, making it efficient and flexible. SAM's overall design prioritizes efficiency, with the prompt encoder and mask decoder running seamlessly in web browsers in approximately 50 milliseconds, enabling real-time interactive prompting. How to Use the Segment Anything Model? At Encord, we see the Segment Anything Model (SAM) as a game changer in AI-assisted labeling. It basically eliminates the need to go through the pain of segmenting images with polygon drawing tools and allows you to focus on the data tasks that are more important for your model.  These other data tasks include mapping the relationships between different objects, giving them attributes that describe how they act, and evaluating the training data to ensure it is balanced, diverse, and bias-free. Enhancing Manual Labeling with AI SAM can be used to create AI-assisted workflow enhancements and boost productivity for annotators. Here are just a few improvements we think SAM can contribute: Improved accuracy: Annotators can achieve more precise and accurate labels, reducing errors and improving the overall quality of the annotated data. Faster annotation: There is no doubt that SAM will speed up the labeling process, enabling annotators to complete tasks more quickly and efficiently when combined with a suitable image annotation tool. Consistency: Having all annotators use a version of SAM would ensure consistency across annotations, which is particularly important when multiple annotators are working on the same project. Reduced workload: By automating the segmentation of complex and intricate structures, SAM significantly reduces the manual workload for annotators, allowing them to focus on more challenging and intricate tasks. Continuous learning: As annotators refine and correct SAM's assisted labeling, we could implement it such that the model continually learns and improves, leading to better performance over time and further streamlining the annotation process. As such, integrating SAM into the annotation workflow is a no-brainer from our side, and it would allow our current and future customers to accelerate the development of cutting-edge computer vision applications. AI-Assisted Labeling with Segment Anything Model on Encord Consider the medical image example from before to give an example of how SAM can contribute to AI-assisted labeling. We uploaded the DICOM image to the demo web UI, and spent 10 seconds clicking the image to segment the different areas of interest. Afterward, we did the same exercise with manual labeling using polygon annotations, which took 2.5 minutes. A 15x improvement in the labeling speed! Read the blog How to use SAM to Automate Data Labeling in Encord for more information.   Combining SAM with Encord Annotate harnesses SAM's versatility in segmenting diverse content alongside Encord's robust ontologies, interactive editing capabilities, and extensive media compatibility. Encord seamlessly integrates SAM for annotating images, videos, and specialized data types like satellite imagery and DICOM files. This includes support for various medical imaging formats, such as X-ray, CT, and MRI, requiring no extra input from the user. How to Fine-Tune Segment Anything Model (SAM) To fine-tune the SAM model effectively, particularly considering its intricate architecture, a pragmatic strategy involves refining the mask decoder. Unlike the image encoder, which boasts a complex architecture with numerous parameters, the mask decoder offers a streamlined and lightweight design. This characteristic makes it inherently easier, faster, and more memory-efficient to fine-tune. Users can expedite the fine-tuning process while conserving computational resources by prioritizing adjustments to the mask decoder. This focused approach ensures efficient adaptation of SAM to specific tasks or datasets, optimizing its performance for diverse applications. The steps include: Create a custom dataset by extracting the bounding box coordinates (prompts for the model) and the ground truth segmentation masks. Prepare for fine-tuning by converting the input images to PyTorch tensors, a format SAM's internal functions expect. Run the fine-tuning step by instantiating a training loop that uses the lightweight mask decoder to iterate over the data items, generate masks, and compare them to your ground truth masks. The mask decoder is easier, faster, and more memory-efficient to fine-tune. Compare your tuned model to the original model to make sure there’s indeed a significant improvement. To see the fine-tuning process in practice, check out our detailed blog, How To Fine-Tune Segment Anything, which also includes a Colab notebook as a walkthrough. Real-World Use Cases and Applications SAM can be used in almost every single segmentation task, from instance to panoptic. We’re excited about how quickly SAM can help you pre-label objects with almost pixel-perfect segmentation masks before your expert reviewer adds the ontology on top.  From agriculture and retail to medical imagery and geospatial imagery, the AI-assisted labeling that SAM can achieve is endless. It will be hard to imagine a world where SAM is not a default feature in all major annotation tools. This is why we at Encord are very excited about this new technology. Find other applications that could leverage SAM below.  Image and Video Editors SAM’s outstanding ability to provide accurate segmentation masks for even the most complex videos and images can provide image and video editing applications with automatic object segmentation skills. Whether the prompts (point coordinates, bounding boxes, etc.) are in the foreground or background, SAM uses positional encodings to indicate if the prompt is in the foreground or background. Generating Synthetic Datasets for Low-resource Industries One challenge that has plagued computer vision applications in industries like manufacturing is the lack of datasets. For example, industries building car parts and planning to detect defects in the parts along the production line cannot afford to gather large datasets for that use case. You can use SAM to generate synthetic datasets for your applications. If you realize SAM does not work particularly well for your applications, an option is to fine-tune it on existing datasets. Interested in synthetic data? Read our article, What Is Synthetic Data Generation and Why Is It Useful? Gaze-based Segmentation AR applications can use SAM’s Zero-Shot Single Point Valid Mask Evaluation technique to segment objects through devices like AR glasses based on where subjects gaze. This can help AR technologies give users a more realistic sense of the world as they interact with those objects. Medical Image Segmentation SAM's application in medical image segmentation addresses the demand for accurate and efficient ROI delineation in various medical images. While manual segmentation is accurate, it's time-consuming and labor-intensive. SAM alleviates these challenges by providing semi- or fully automatic segmentation methods, reducing time and labor, and ensuring consistency.  With the adaptation of SAM to medical imaging, MedSAM emerges as the first foundation model for universal medical image segmentation. MedSAM leverages SAM's underlying architecture and training process, providing heightened versatility and potentially more consistent results across different tasks. The MedSAM model consistently does better than the best segmentation foundation models when tested on a wide range of internal and external validation tasks that include different body structures, pathological conditions, and imaging modalities. This underscores MedSAM's potential as a powerful tool for medical image segmentation, offering superior performance compared to specialist models and addressing the critical need for universal models in this domain. MedSAM is open-sourced, and you can find the code for MedSAM on GitHub. Where Does This Leave Us? The Segment Anything Model (SAM) truly represents a groundbreaking development in computer vision. By leveraging promptable segmentation tasks, SAM can adapt to a wide variety of downstream segmentation problems using prompt engineering.  This innovative approach, combined with the largest labeled segmentation dataset to date (SA-1B), allows SAM to achieve state-of-the-art performance in various segmentation tasks. With the potential to significantly enhance AI-assisted labeling and reduce manual labor in image segmentation tasks, SAM can pave the way in industries such as agriculture, retail, medical imagery, and geospatial imagery.  At Encord, we recognize the immense potential of SAM, and we are soon bringing the model to the Encord Platform to support AI-assisted labeling, further streamlining the data annotation process for users. As an open-source model, SAM will inspire further research and development in computer vision, encouraging the AI community to push the boundaries of what is possible in this rapidly evolving field.  Ultimately, SAM marks a new chapter in the story of computer vision, demonstrating the power of foundation models in transforming how we perceive and understand the world around us. Frequently Asked Questions on Segment Anything Model (SAM) How do I fine-tune SAM for my tasks? We have provided a step-by-step walkthrough you can follow to fine-tune SAM for your tasks. Check out the tutorial: How To Fine-Tune Segment Anything. What datasets were used to train SAM? The Segment Anything 1 Billion Mask (SA-1B) dataset has been touted as the “ImageNet of segmentation tasks.” The images vary across subject matter. Scenes, objects, and places frequently appear throughout the dataset. Masks range from large-scale objects such as buildings to fine-grained details like door handles. See the data card and dataset viewer to learn more about the composition of the dataset. Does SAM work well for all tasks? Yes. You can automatically select individual items from images—it works very well on complex images. SAM is a foundation model that provides multi-task capabilities to any application you plug it into. Does SAM work well for ambiguous images? Yes, it does. Because of this, you might find duplicates in your mask sets when you run SAM over your dataset. This allows you to select the most appropriate masks for your task. In this case, you should add a post-processing step to segment the most suitable masks for your task. How long does it take SAM to generate segmentation masks? SAM can generate a segment in as little as 50 milliseconds—practically real-time! Do I need a GPU to run SAM? Although it is possible to run SAM on your CPU, you can use a GPU to achieve significantly faster results from SAM.

If you're looking for an image annotation tool, you have plenty of choices. The market is saturated, making it challenging to find the best tool for your needs. To help you out, we did much of the research for you to streamline your buying process. In this article, you will find a detailed overview of the most popular data annotation tools, including: Encord, Amazon SageMaker Ground Truth, Scale Rapid, Supervisely, CVAT, Labelbox, Playment, Appen, Dataloop, SuperAnnotate, V7, Hive, Label Studio, COCO Annotator, Make Sense, VGG Image Annotator, and, LabelMe Best Image Annotation Tools The sections below give an overview of the key features and user reviews for the above mentioned tools. The summary table below compares all the tools based on supported data types, annotation types, ease of use, and automation. Encord Encord is an end-to-end data development platform with an advanced image annotation tool for complex computer vision and multimodal use cases. The platform offers state-of-the-art model-assisted labeling and customizable workflows to accelerate image annotation projects and build production-ready models. Key Features AI-assisted labeling: Automate 97% of your image annotations with 99% accuracy by leveraging SOTA automated labeling capabilities such as Meta AI’s Segment Anything Model (SAM). Full suite of tools: Encord supports a range of labeling options, such as bounding boxes, rotatable boxes, polygons, polylines, key points, and classifications to support your model requirements. Accelerate with models-in-the-loop: Bring your own model to the Encord platform or leverage one of our Agents to pre-label datasets. Scalability: Encord lets you scale AI projects by supporting extensive datasets of up to 500,000 images.  Build balanced datasets: Filter and slice datasets in a consolidated visual explorer and export for labeling in one click. Encord supports deep search, filtering, and metadata analysis. Complex ontologies: Build nested relationship structures in your data schema to improve the quality of your model output. Bulk classification: Leverage natural language or similarity search to select large datasets and label en masse, queue for review to accelerate labeling operations. Build reliable quality control workflows: Build robust workflows with multi-step review stages and consensus benchmarking for quality assurance. Find and fix label errors: Automatically surface labeling errors to shift your attention to the labels impacting model performance. Collaboration: Control user roles with permissions, manage task assignments and infinitely scale your MLOps workflows. Enterprise-grade security as standard: Encord Annotate complies with the General Data Protection Regulation (GDPR), System and Organization Controls 2 (SOC 2), and Health Insurance Portability and Accountability Act (HIPAA) standards while using advanced encryption protocols to ensure data privacy. Integrations: Encord allows you to retain total control of your data. Securely connect your native cloud storage buckets and programmatically control workflows. Advanced Python SDK and API access with easy export into JSON and COCO formats. Integrated Data Labeling Services: Outsource your labeling tasks to an expert workforce of vetted, trained, and specialized annotators. Encord G2 Review Summary Encord has a rating of 4.8/5 based on 60 reviews. Users prefer Encord’s powerful ontology feature, which lets them define rich taxonomy for all data sizes. In addition, the platform’s collaborative features and granular annotation tools help users improve annotation quality. Learn how Encord Annotate helps you create rich ontologies for an efficient labeling process. Curious? Try it out Amazon SageMaker Ground Truth Amazon SageMaker Ground Truth is a human-in-the-loop data labeling platform that offers features to label large datasets. It provides a self-serve and a managed service option to help you streamline your annotation workflow for multiple CV tasks. Amazon SageMaker Ground Truth Key Features Data Generation: The platform offers tools to fine-tune pre-trained models on a few data points to generate synthetic data samples for more diverse training. Model Evaluation: Sagemaker Ground Truth lets you evaluate foundation models based on multiple metrics such as accuracy, relevancy, toxicity, and bias through human feedback. Labeling Templates: It features over thirty labeling templates for multiple CV and NLP tasks, including image classification, object detection, text classification, and named entity recognition (NER). Interactive Dashboards: The tool offers intuitive dashboards and user-friendly interfaces to monitor labeling progress across multiple projects. G2 Review Summary Amazon SageMaker Ground Truth has a rating of 4.1/5 based on 19 reviews. Users like its ease of use and advanced annotation capabilities. However, they feel it is expensive, and tracking labeling performance is challenging. Scale Rapid Scale Rapid is a data and labeling services platform that supports computer vision use cases. It specializes in reinforcement learning with human feedback (RLHF), user experience optimization, large language models (LLMs), and synthetic data. Scale Rapid Key Features Supported Data Types: Scale lets you annotate text, images, video, audio, and point-cloud data. 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. G2 Review Summary Scale Rapid has a rating of 4.4/5 based on 11 reviews. Users say it is easy to learn and does not require complex installation procedures. However, they feel the user interface is clunky and the tool’s pricing is complex. Find out about the top tools that help you perform reinforcement learning with human feedback (RLHF). Supervisely Supervisely is an end-to-end computer vision platform that offers multiple annotation tools for labeling images and videos. It features AI-based labeling that lets users automate labeling workflow through advanced ML models. Supervisely Key Features Versatile Annotation Tool: It supports multiple annotation types, including bounding boxes, polygons, polylines, points, and segmentation masks for precise labeling. Supported Data Types: Supervisely lets you label images, videos, point cloud, and medical image data. Smart Labeling Tools: Feature a class-agnostic smart tool based on customizable neural networks for capturing any object type, depending on your use case. Collaboration: The platform lets you collaborate with team members and assign relevant user roles to track issues and labeling performance. G2 Review Summary Supervisely has a rating of 4.7/5 based on ten reviews. Users like the tool’s integration with multiple apps within the Supervisely ecosystem, giving a smooth user experience. However, the number of options can be overwhelming, and the platform has latency issues. CVAT (Computer Vision Annotation Tool) CVAT is an open-source web-based image annotation tool by Intel. In 2022, CVAT’s data, content, and GitHub repository became a part of OpenCV, where CVAT continues to be open-source. Furthermore, CVAT can also help annotate QR codes within images, facilitating the integration of QR code recognition into computer vision pipelines and applications. CVAT Key Features Manual Annotation Tools: The tool supports various annotation types, including bounding boxes, polygons, polylines, points, and cuboids, catering to diverse annotation needs. Multi-platform Compatibility: Works on multiple operating systems such as Windows, Linux, and macOS, providing flexibility for users. Export Formats: CVAT supports numerous data formats, including JSON, COCO, and Pascal VOC, ensuring annotation compatibility with diverse tools and platforms. Automated Labeling: CVAT supports multiple algorithms, including the Segment Anything Model (SAM), YOLOv3, and Deep Extreme Cut (DEXTR). G2 Review Summary CVAT has a rating of 4.5/5 based on two reviews. Users like that the tool is free to use and requires no configuration and installation process because it is web-based. However, its slow performance and backend server failure are the most significant concerns. Labelbox Labelbox is a US-based data annotation platform founded in 2017 that provides a unified framework for curating and labeling datasets with collaboration and model evaluation tools.  Besides a stand-alone image labeling platform, the tool offers managed annotation services with data labeling experts. Labelbox Key Features Data Management: Labelbox offers QA workflows and data annotator performance tracking. Customizable Labeling Interface: It features a user-friendly interface, providing easy-to-navigate editors for specific needs. Automation: Allows integration with AI models for automatic data labeling to accelerate the annotation process. Annotation Capabilities: It supports annotation for multiple data types beyond images, including text, video, audio, geospatial and medical images. G2 Review Summary LabelBox has a rating of 4.7/5 based on 33 reviews. Users find the tool’s data management features helpful. However, they feel that it does not perform well with high-resolution images. Playment Playment is an Indian-based end-to-end data annotation platform founded in 2015 and now operating under Telus’ ownership. It offers managed annotation services by employing computer vision teams to annotate training data for multiple use cases. Playment Key Features Data Labeling Services: Provides high-quality data labeling services for various data types, including images, videos, text, and sensor data. Support: Global workforce of contractors and data labelers. Scalability: Capable of handling large-scale annotation projects and accommodating growing datasets and annotation needs. Audio Labeling Tool: The tool features a speech recognition training platform that can handle over five hundred languages and dialects. G2 Review Summary Playment has a rating of 4.7/5 based on 11 reviews. Users find Playment’s annotation performance fast and accurate. However, they find the tool expensive and that it needs more improvement in automated labeling features. Appen Appen is a data labeling services platform founded in 1996, making it one of the first and oldest solutions in the market, offering data labeling services for various industries. In 2019, it acquired Figure Eight to expand its software capabilities and help businesses train and improve their computer vision models. Appen 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: Support for natural language processing (NLP) 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. G2 Review Summary Appen has a rating of 4.2/5 based on 28 reviews. Users like that the tool is web-based and does not require specific installation procedures. However, the platform’s server crashes frequently, and the support team is slow to respond. Dataloop 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 Key Features Data Annotation: Supports multiple image annotation tasks, including classification, detection, and semantic segmentation. Collaboration Tool: It features tools for real-time collaboration among annotators, project sharing, and version control, allowing for efficient teamwork. Data Management: Offers data management capabilities, including data versioning, tracking, and organization for streamlined workflows. Model Management: Dataloop offers tools to manage different model versions and download SOTA models from the Model Marketplace. G2 Review Summary Dataloop has a rating of 4.4/5 based on 90 reviews. The tool’s plus points include its ease of use and annotation efficiency. However, users find it challenging to learn and face frequent performance issues. SuperAnnotate SuperAnnotate is an end-to-end AI platform that offers tools for data curation and automatic annotation with MLOps functionalities. It also lets you fine-tune LLMs using annotated data and RLHF. SuperAnnotate Key Features Multi-Data Type Support: Versatile annotation features for labeling videos, text, audio, and image data. AI Assistance: Integrates AI-assisted annotation to accelerate the labeling process and improve efficiency. Customization: Provides customizable annotation interfaces and workflows to tailor annotation tasks to specific project requirements. Export Formats: SuperAnnotate supports multiple data formats, including popular ones like JSON, COCO, and Pascal VOC. G2 Review Summary SuperAnnotate has a rating of 4.9/5 based on 137 reviews. Users find the tool’s feature set comprehensive and the interface intuitive. However, there have been complaints regarding its custom workflow setup and high price. V7 Labs V7 is a UK-based data annotation platform founded in 2018. The company enables teams to annotate image and video data using automated pipelines and custom workflows. The platform also offers model and data management tools to help users build high-quality training data for scalable AI projects. V7 Key Features Collaboration Capabilities: Project management and automation workflow functionality, with real-time collaboration and tagging. Data Management: The tool offers data management features, including functionalities to filter and sort data. It also helps organize and manage data classes at team and dataset levels. Auto-Annotate: Features auto-annotation that lets you use deep learning models to create pixel-perfect polygon masks. Auto-Track: V7 offers an auto-track feature for object tracking and instance segmentation in long videos. G2 Review Summary V7 has a rating of 4.8/5 based on 52 reviews. Users find its automation and collaboration features significantly helpful. However, they feel it lacks file manipulation options, and its sorting and filtering features do not perform well with large files. Hive Hive is a content-moderation platform that offers deep learning models to highlight harmful and explicit content in images, videos, text, and audio. It also features search and generative APIs to visualize similarities between images and videos and generate images based on textual prompts. Hive AI Key Features Ease of use: Hive offers an intuitive interface with multiple in-built image and text classification models. Embeddings: The platform lets you quickly create text embeddings to build retrieval augmented generation (RAG)-based LLMs. Search: Hive offers versatile web search functionality. You can use image prompts to retrieve relevant links to similar images. Generative Artificial Intelligence (Gen AI): Hive features APIs to generate text, images, and videos based on textual prompts. G2 Review Summary Hive has a rating of 4.6/5 based on 528 reviews. Users find its project management and collaboration features helpful. However, the interface is challenging to navigate and has a few glitches, which makes it complex to operate. Label Studio Label Studio is a popular open-source data labeling platform for annotating various data types, including images, text, audio, and video. It supports collaborative labeling, custom labeling interfaces, and integration with machine learning (ML) pipelines for data annotation tasks. Label Studio Key Features Customizable Labeling Interfaces: Label Studio lets you label data through flexible configurations that allow you to tailor annotation interfaces to specific tasks. Collaboration Tools: Real-time annotation and project-sharing capabilities for seamless collaboration among annotators. 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. ML Pipelines: Label Studio lets you connect the model development pipeline with the data labeling project. The method allows you to use ML models to predict labels, evaluate model performance, and perform human-in-the-loop labeling. G2 Review Summary G2 review not available. COCO Annotator COCO Annotator is a web-based labeling tool by Justin Brooks that is under the MIT license. The tool helps streamline the process of annotating images for object recognition, localization, and key point detection models. It also offers a range of features that cater to the diverse needs of machine learning practitioners, data scientists, and researchers. COCO Annotator Key Features Image Annotation: Supports annotation of images for object detection, instance segmentation, keypoint detection, and captioning tasks. Export Formats: The tool exports and stores annotations in the COCO format to facilitate large-scale object detection. Automation: The tool makes annotating an image easier by incorporating semi-trained models. It also provides access to advanced selection tools, including the Mask Region-based Convolutional Neural Network (MaskRCNN), Magic Wand, and Deep Extreme Cut (DEXTR) frameworks. Metadata Management: Users can create custom metadata for each instance or object. G2 Review Summary G2 review not available. Make Sense Make Sense AI is a user-friendly open-source annotation tool available under the GPLv3 license. It is accessible through a web browser and does not require advanced installations. The tool simplifies the annotation process for multiple image types. Make Sense 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, VGG JSON, and CSV), ensuring compatibility with diverse machine learning algorithms. Supported Annotation Types: The tool supports rectangles, lines, points, and polygons. G2 Review Summary G2 review not available. VGG Image Annotator VGG Image Annotator (VIA) is a versatile open-source tool by the Visual Geometry Group (VGG) for manually annotating image and video data. Released under the permissive BSD-2 clause license, VIA serves the needs of academic and commercial users, offering a lightweight and accessible solution for annotation tasks. VIA Key Features Lightweight and User-Friendly: VIA is a lightweight, self-contained annotation tool that uses HTML, Javascript, and CSS without external libraries. Offline Capability: The tool works offline, providing a full application experience within a single HTML file of less than 200 KB.  Audio and Video Annotation: In addition to images, the tool lets users define temporal segments in audio and video data with textual descriptions. Supported Annotation Types: The tool allows you to draw rectangles, circles, ellipses, polygons, points, and polylines. G2 Review Summary G2 review not available. LabelMe LabelMe is an open-source web-based tool 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 Key Features Web-Based: Accessible through a web-based interface, allowing you to perform annotation tasks in any modern web browser without requiring software installation. Supported Data Types: The tool supports image and video annotation. Supported Annotation Types: LabelMe lets you draw polygons, rectangles, circles, lines, and points. Export Format: It lets you export annotation in VOC and COCO format for semantic and instance segmentation. G2 Review Summary G2 review not available. Key Takeaways: Image Annotation Tools in 2024 As data volume and variety increase, businesses must invest in a suitable and reliable annotation tool to build high-quality datasets for training models. Below are a few key points regarding the top image annotation tools and tips for selecting an appropriate solution. Best Annotation Tools in 2024: Encord, Amazon Sagemaker Ground Truth, and Scale Rapid are the top annotation tools in 2024. Ease-of-use: Most G2 reviews highlight issues with the user interface. You should ensure that you select a tool that offers intuitive navigation and labeling features. Automation: Select a platform that offers state-of-the-art automation features, including pre-trained models and smart labeling tools. Open-source vs. Paid Platforms: While open-source tools offer a cost-effective solution, they have limited functionality. Paid tools provide a rich feature set with robust customer support to help you annotate multiple data types. So, streamline your CV operations with the annotation tool that best suits your needs.