5 Questions to Ask When Evaluating a Video Annotation Tool

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.

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

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