Google’s Video Gaming Companion: Scalable Instructable Multiworld Agent [SIMA]

Mistral AI made headlines with the release of Mistral 7B, an open-source model competing with tech giants like OpenAI and Meta and surpassing several state-of-the-art large language models such as LLaMA 2. Now, in collaboration with Microsoft, the French AI startup introduces Mistral Large, marking a significant advancement in language model development and distribution. What Is Mistral Large? Mistral Large, developed by Mistral AI, is an advanced language model renowned for its robust reasoning capabilities tailored for intricate multilingual tasks. Fluent in English, French, Spanish, German, and Italian, it exhibits a nuanced grasp of various languages. Boasting a 32K tokens context window, Mistral Large ensures precise information retrieval from extensive documents, facilitating accurate and contextually relevant text generation. With the incorporation of retrieval augmented generation (RAG), it can access facts from external knowledge bases, thereby enhancing comprehension and precision. Mistral Large also excels in instruction-following and function-calling functionalities, enabling tailored moderation policies and application development. Its performance in coding, mathematical, and reasoning tasks makes it a notable solution in natural language processing. Key Attributes of Mistral Large Reasoning Capabilities: Mistral Large showcases powerful reasoning capabilities, enabling it to excel in complex multilingual reasoning tasks. It stands out for its ability to understand, transform, and generate text with exceptional precision. Native Multilingual Proficiency: With native fluency in English, French, Spanish, German, and Italian, Mistral Large demonstrates a nuanced understanding of grammar and cultural context across multiple languages. Enhanced Contextual Understanding: Featuring a 32K tokens context window, Mistral Large offers precise information recall from large documents, facilitating accurate and contextually relevant text generation. Mistral Large, unlike Mistral 7B, the open-sourced LLM that provided stiff competition to state-of-the-art (SOTA) large language models, is equipped with retrieval augmented generation (RAG). This feature enables the LLM to retrieve facts from an external knowledge base, grounding its understanding and enhancing the accuracy and contextuality of its text-generation capabilities. Instruction-Following Mistral Large's instruction-following capabilities allow developers to design customized moderation policies and system-level moderation, exemplified by its usage in moderating platforms like le Chat. Function Calling Capability Mistral Large can directly call functions, making it easier to build and update apps and tech stack modernization on a large scale. With this feature and limited output mode, developers can add advanced features and make interactions smoother without any hassle. For more information, read the blog What is Retrieval Augmented Generation (RAG)?   Performance Benchmark The performance of Mistral Large is compared on various tasks against other state-of-the-art LLM models which are commonly used as benchmarks. Reasoning and Knowledge These benchmarks assess various aspects of language understanding and reasoning, including tasks like understanding massive multitask language (MMLU), completing tasks with limited information (e.g., 5-shot and 10-shot scenarios), and answering questions based on different datasets (e.g., TriviaQA and TruthfulQA). Multi-lingual Capacities The multilingual capability of Mistral Large undergoes benchmarking on HellaSwag, Arc Challenge, and MMLU benchmarks across French, German, Spanish, and Italian languages. Its performance is compared to Mistral 7B and LLaMA 2. Notably, Mistral Large hasn't been tested against the GPT series or Gemini, as these language models have not disclosed their performance metrics on these 4 languages. To know more about the Mistral 7B, read the blog Mistral 7B: Mistral AI's Open Source Model.   Maths and Coding Mistral Large excels across coding and math benchmarks, showcasing strong problem-solving abilities. With high pass rates in HumanEval and MBPP, it demonstrates proficiency in human-like evaluation tasks. Achieving a majority vote accuracy of 4 in the Math benchmark and maintaining accuracy in scenarios with limited information in GSM8K benchmarks, Mistral Large proves its effectiveness in diverse mathematical and coding challenges. Comparison of Mistral Large with other SOTA Models Mistral Large demonstrates impressive performance on widely recognized benchmarks, securing its position as the second-ranked model available via API globally, just behind GPT-4.  Detailed comparisons against other state-of-the-art (SOTA) models like Claude 2, Gemini Pro 1.0, GPT 3.5, and LLaMA 2 70B are provided on benchmarks such as MMLU (Measuring massive multitask language understanding), showcasing Mistral Large's competitive edge and advanced capabilities in natural language processing tasks. Mistral Large: Platform Availability La Plataforme Hosted securely on Mistral's infrastructure in Europe, La Plateforme offers developers access to a comprehensive array of models for developing applications and services. This platform provides a wide range of tools and resources to support different use cases. Le Chat Le Chat serves as a conversational interface for interacting with Mistral AI's models, providing users with a pedagogical and enjoyable experience to explore the company's technology. It can utilize Mistral Large or Mistral Small, as well as a prototype model called Mistral Next, offering brief and concise interactions. Microsoft Azure Mistral AI has announced its partnership with Microsoft and made Mistral LArge available in Azure AI Studio providing users with a user-friendly experience similar to Mistral's APIs. Beta customers have already experienced notable success utilizing Mistral Large on the Azure platform, benefiting from its advanced features and robust performance. Self-deployment For sensitive use cases, Mistral Large can be deployed directly into the user's environment, granting access to model weights for enhanced control and customization. Mistral Large on Microsoft Azure Mistral Large is set to benefit significantly from the multi-year partnership of Microsoft with Mistral AI on three key aspects: Supercomputing Infrastructure: Microsoft Azure will provide Mistral AI with supercomputing infrastructure tailored for AI training and inference workloads, ensuring best-in-class performance and scalability for Mistral AI's flagship models like Mistral Large. This infrastructure will enable Mistral AI to handle complex AI tasks efficiently and effectively. Scale to Market: Through Models as a Service (MaaS) in Azure AI Studio and Azure Machine Learning model catalog, Mistral AI's premium models, including Mistral Large, will be made available to customers. This platform offers a diverse selection of both open-source and commercial models, providing users with access to cutting-edge AI capabilities. Additionally, customers can utilize Microsoft Azure Consumption Commitment (MACC) for purchasing Mistral AI's models, enhancing accessibility and affordability for users worldwide. AI Research and Development: Microsoft and Mistral AI will collaborate on AI research and development initiatives, including the exploration of training purpose-specific models for select customers. This collaboration extends to European public sector workloads, highlighting the potential for Mistral Large and other models to address specific customer needs and industry requirements effectively. Mistral Small Mistral Small, introduced alongside Mistral Large, represents a new optimized model specifically designed to prioritize low latency and cost-effectiveness. This model surpasses Mixtral 8x7B, the sparse mixture-of-experts network, in performance while boasting lower latency, positioning it as a refined intermediary solution between Mistral's open-weight offering and its flagship model. Mistral Small inherits the same innovative features as Mistral Large, including RAG-enablement and function calling capabilities, ensuring consistent performance across both models. To streamline their endpoint offering, Mistral is introducing two main categories: Open-weight Endpoints: These endpoints, named open-mistral-7B and open-mixtral-8x7b, offer competitive pricing and provide access to Mistral's models with open weights, catering to users seeking cost-effective solutions. New Optimized Model Endpoints: Mistral is introducing new optimized model endpoints, namely mistral-small-2402 and mistral-large-2402. These endpoints are designed to accommodate specific use cases requiring optimized performance and cost efficiency. Also, mistral-medium will be maintained without updates at this time. To know more about the Mistral AI LLM models and how to access them, read the documentation.   Mistral Large: What’s Next? Multi-currency Pricing Moving forward, Mistral AI is introducing multi-currency pricing for organizational management, providing users with the flexibility to transact in their preferred currency. This enhancement aims to streamline payment processes and improve accessibility for users worldwide. Reduced End-point Latency Mistral AI states that it is working to reduce the latency of all our endpoints. This improvement ensures faster response times, enabling smoother interactions and improved efficiency for users across various applications. La Plataforme Service Tier Updates To make their services even better, Mistral AI has updated the service tiers on La Plataforme. These updates aim to improve performance, reliability, and user satisfaction for those using Mistral AI's platform for their projects and applications.

 What is Claude 3? Claude 3 is a family of large multimodal models by Anthropic: Claude 3 Opus, Claude 3 Sonnet, Claude 3 Haiku. Opus excels in various domains. Sonnet balances skills and speed. Haiku prioritizes speed and affordability. All models process text and images, offer improved multilingual fluency, and undergo comprehensive evaluations for safety. Joining the tech race of building AI chatbots with OpenAI’s ChatGPT, Google’s Gemini 1.5, or Le Chat of Mistral AI, Anthropic has introduced Claude. Claude is an AI assistant that helps manage organizations' tasks no matter the scale. The Claude 3 model family shows better performance than other SOTA models.  Claude 3 sets new benchmarks across reasoning, math, coding, multi-lingual understanding, and vision quality. Leveraging unsupervised learning and Constitutional AI, trained on AWS and GCP hardware with PyTorch, JAX, and Triton frameworks. Claude 3’s AI Model Suite Each large language model within the Claude 3 family is tailored to offer different combinations of capabilities, speed, and cost-effectiveness. Claude 3 Opus It is the most capable offering, achieving state-of-the-art results on benchmark evaluations across various domains such as reasoning, math, and coding. It sets new standards in performance and is suitable for applications requiring high levels of intelligence and processing power. Claude 3 Sonnet It provides a balance between skills and speed, offering strong performance in cognitive tasks while being more efficient in terms of processing time compared to Opus. Claude 3 Haiku It is the fastest and least expensive model in the family, suitable for applications where speed and cost-effectiveness are prioritized over absolute performance. Intelligence Benchmark Scores Vs. Cost Comparison of Claude 3 Model Family All models in the Claude 3 family come with vision capabilities for processing image data and exhibit improved fluency in non-English languages, making them versatile for a global audience. Model Training Data and Process The Claude 3 models are trained using a blend of publicly available internet data as of August 2023, along with public data from data labeling services and synthetic data generated internally. The training process involves several data cleaning and filtering methods, including deduplication and classification. The models are not trained on any user-submitted prompt or output data. Anthropic follows industry practices when obtaining data from public web pages, respecting robots.txt instructions and other signals indicating whether crawling is permitted. The crawling system operates transparently, allowing website operators to identify Anthropic visits and signal their preferences. The training of Claude 3 models emphasizes being helpful, harmless, and honest. Techniques include pretraining on diverse data sets for language capabilities and incorporating human feedback to elicit desirable responses. Constitutional AI, including principles from sources like the UN Declaration of Human Rights, ensures alignment with human values. A principle promoting respect for disability rights is integrated into Claude's constitution. Human feedback data, including publicly available sources, is used for finetuning. For more information on RLHF, read the blog Guide to Reinforcement Learning from Human Feedback (RLHF) for Computer Vision.   Performance Benchmark: Claude 3, GPT-4, GPT-3.5, Gemini Ultra, and Gemini Pro Claude 3, particularly the Opus model, surpasses other state-of-the-art models in various evaluation benchmarks for AI tools. It excels in domains such as undergraduate and graduate-level expert knowledge (MMLU, GPQA), basic mathematics (GSM8K), and more. Opus demonstrates near-human levels of comprehension and fluency, positioning itself at the forefront of general intelligence. Compared to other models like OpenAI’s GPT-4, GPT-3.5, Gemini Ultra, and Gemini Pro, Claude 3 models showcase enhanced capabilities in diverse areas. These include analysis and forecasting, nuanced content creation, code generation, and multilingual conversation proficiency in languages such as Spanish, Japanese, and French. Performance Benchmark Scores of Claude 3 Model Family: Opus, Sonnet, Haiku Claude 3 Capabilities Vision Capabilities: Photos, Charts, Graphs and Technical Diagrams The Claude 3 models are equipped to process and interpret visual information along with text inputs. The vision capabilities are particularly showcased in tasks like the AI2D science diagram benchmark and visual question answering. They excel in parsing scientific diagrams and achieving high accuracy rates in both zero-shot and few-shot settings. Evaluation Results on Multimodal Tasks Trained on diverse visual data, Claude 3 models effectively interpret and analyze various visual content, enhancing their overall problem-solving capabilities for applications in fields like image understanding and multimodal reasoning. Near Instant Model Results Claude 3 models deliver near-instant results, ideal for live customer chats, auto-completions, and data extraction tasks. Haiku is the fastest and most cost-effective, processing dense research papers in under three seconds. Sonnet is twice as fast as previous versions, suitable for rapid tasks like knowledge retrieval. Opus matches previous speeds but with higher intelligence levels. Multimodal Claude 3 shows impressive multimodal capabilities, adept at processing diverse types of data. Claude 3 excels in visual question answering, demonstrating its capacity to understand and respond to queries based on images. It showcases strong quantitative reasoning skills by analyzing and deriving insights from visual data, enhancing its overall versatility across various tasks. Multilingual Understanding Claude 3 showcases robust multilingual capabilities, important for global accessibility. Evaluations highlight Claude 3 Opus's state-of-the-art performance in the Multilingual Math MGSM benchmark, achieving over 90% accuracy in a zero-shot setting. Human feedback shows significant improvement in Claude 3 Sonnet, indicating enhanced multilingual reasoning capabilities compared to previous versions. The Claude 3 Model Family: Multilingual Capabilities Factual Accuracy Claude 3 prioritizes factual accuracy through rigorous evaluations, including 100Q Hard and Multi-factual datasets. Tracking correctness, incorrect, and unsure responses, Claude 3 Opus significantly improves accuracy over previous versions. Factual Accuracy of Claude 3 Models Vs Claude 2.1 Reasoning and Mathematical Problem Solving Claude 3 exhibits remarkable reasoning and mathematical problem-solving abilities, surpassing previous models in various benchmarks. In evaluations such as GPQA and MATH, Claude 3 Opus achieves significant improvements, although falling slightly short of expert-level accuracy. Leveraging techniques like chain-of-thought reasoning and majority voting further enhances performance, with Opus demonstrating impressive scores in both reasoning and mathematical problem-solving tasks, showcasing its advanced capabilities in these domains. Near-human Comprehension Claude 3 Sonnet outperforms its predecessors, Claude 2 and Claude Instant, in various core tasks, as assessed through direct comparisons by human raters. It excels in writing, coding, long document Q&A, non-English conversation, and instruction following. Domain experts across finance, law, medicine, STEM, and philosophy prefer Sonnet in 60-80% of cases. Human feedback, although noisy, provides insights into user preferences that industry benchmarks may overlook. Using Elo scores, Sonnet shows a significant improvement of roughly 50-200 points over Claude 2 models in various subject areas.  Claude models exhibit high proficiency in open-ended conversation, coding tasks, and text-related operations like searching, writing, and summarizing. They also interpret visual input for enhanced productivity and maintain a helpful, conversational tone, described as steerable, adaptive, and engaging by users. Claude's prediction mechanism constructs responses sequentially based on the input and past conversation, unable to edit previous responses or access external information beyond its context window, achieving near-human comprehension in various tasks. Contextual Understanding and Fewer Refusals Unlike previous versions, Claude 3 models are less likely to refuse to answer prompts that are within their capabilities and ethical boundaries. This improvement indicates a more refined understanding of context and a reduction in unnecessary refusals, enhancing their overall performance and usability. Comparison of Incorrect Refusals: Claude 3 Model Family Vs. Claude 2.1 Information Recall from Long Context Claude 3's capability for information recall from long contexts is impressive, expanding from 100K to 200K tokens and supporting contexts up to 1M tokens. Despite challenges in reliable recall within long contexts, Claude 3 models, particularly Claude Opus, exhibit significant improvements in accurately retrieving specific information. In evaluations like Needle In A Haystack (NIAH), Claude Opus consistently achieves over 99% recall in documents of up to 200K tokens, highlighting its enhanced performance in information retrieval tasks. Information Recall: Claude 3 Model Family (Opus, Sonnet, Haiku) Vs. Claude 2 Improved Accuracy Improved accuracy in Claude 3 models is important for businesses relying on them to serve customers at scale. Evaluation involves a large set of complex, factual questions targeting known weaknesses in previous models.  Accuracy Comparison: Claude 3 Model Family (Opus, Sonnet, Haiku) Vs. Claude 2 Claude 3 Opus demonstrates a twofold improvement in accuracy, reducing incorrect answers and admitting uncertainty when necessary. The upcoming features like citations will enhance trustworthiness by enabling precise verification of answers from reference material. For more information, read the model card:The Claude 3 Model Family: Opus, Sonnet, Haiku   Model Details Claude 3: Model Availability Opus and Sonnet are currently available for use in the Anthropic API, enabling developers to sign up and start using these models immediately. Haiku will be available soon. Sonnet powers the free experience on claude.ai, while Opus is available for Claude Pro subscribers. Sonnet is available through Amazon Bedrock, with Opus and Haiku coming soon to both Amazon Bedrock and Google Cloud's Vertex AI Model Garden in a private preview. Model Costs Claude 3 Opus Claude 3 Opus stands out as the most intelligent model, offering unparalleled performance on complex tasks. It excels in handling open-ended prompts and navigating sight-unseen scenarios with remarkable fluency and human-like understanding, showcasing the outer limits of generative AI. However, this high intelligence comes at a higher cost of $15 per million input tokens and $75 per million output tokens. The context window for Opus is 200K tokens, and it is suitable for tasks such as task automation, research and development, and advanced strategic analysis. Claude 3 Sonnet Claude 3 Sonnet, on the other hand, strikes a balance between intelligence and speed, making it ideal for enterprise workloads. It offers strong performance at a lower cost compared to its peers, with rates of $3 per million input tokens and $15 per million output tokens. Sonnet's context window is also 200K tokens, and it is suitable for data processing, sales tasks, and time-saving operations like code generation. Claude 3 Haiku Claude 3 Haiku is the fastest and most compact model, designed for near-instant responsiveness. It excels in handling simple queries and requests with unmatched speed and affordability, costing $0.25 per million input tokens and $1.25 per million output tokens. Haiku's context window is also 200K tokens, and it is suitable for tasks like customer interactions, content moderation, and cost-saving operations.  The Claude 3 Haiku model is now accessible via Amazon Bedrock on Amazon Web Services.                                                               Responsible Design Risk Mitigation Dedicated teams continuously track and mitigate various risks, including misinformation, harmful content, and potential misuse in areas such as biological information, election integrity, and autonomous replication. Bias Reduction Ongoing efforts focus on reducing biases in model outputs, with Claude 3 demonstrating decreased biases compared to previous models, as measured by the Bias Benchmark for Question Answering (BBQ). Model Neutrality Advanced methods such as Constitutional AI enhance model transparency and neutrality, guaranteeing that results are not biased toward any one political position. Responsible Scaling Policy Claude 3 models are classified at AI Safety Level 2 (ASL-2) under the Responsible Scaling Policy, with rigorous evaluations affirming minimal potential for catastrophic risks at present. Future models will be closely monitored to assess their proximity to ASL-3.  Claude 3: What’s Next Here is what to expect from the new models of Anthropic’s Claude:  Feature Updates for Enterprise Use Case Tool Use or Function Calling: Development is underway to enable Claude 3 to utilize functions, allowing for more advanced task automation and data processing. REPL or Interactive Coding: Claude 3 will soon support an interactive coding environment, providing users with the ability to engage in real-time code execution and debugging. Advanced Agentic Capabilities: Explorations are ongoing to equip Claude 3 with more advanced agentic capabilities, facilitating seamless interaction with users and autonomous execution of complex tasks. Large-scale Deployments: Optimization efforts are being made to ensure Claude 3 is suitable for large-scale deployments, enabling it to handle high volumes of requests while maintaining performance and reliability in enterprise settings. Safety Guardrails with Feature Advancements: In line with feature updates, Claude 3 is also working on its safety protocols to mitigate risks and promote responsible usage. At the same time, the focus remains on leveraging these advancements to foster positive societal outcomes, allowing users to achieve their goals ethically and efficiently while upholding principles of fairness, transparency, and accountability in artificial intelligence.

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