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Top Alternatives to Labelbox
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Encord
iMerit
TELUS International
CVAT
Written by
Nikolaj Buhl
Labelbox is a popular data labeling platform, offering tools for various industries and use cases.
Labelbox labels data like images, text, and documents, making it a good choice for AI and machine learning projects. Key features include data labeling, quality assurance, integration with machine learning frameworks and data management tools, and an intuitive interface.
Yet, Labelbox does come with its own set of constraints, including issues with native video rendering, restricted DICOM compatibility, and a pricing structure that may not adapt effectively to scalability.
For these reasons, we will explore alternatives to Labelbox.
Encord
Encord is a leading alternative platform to build annotation workflows, curate visual data, find and fix data errors, and monitor model performance.
Key Features and Benefits of Encord:
- Encord is a state-of-the-art AI-assisted labeling and workflow tooling platform enriched by micro-models, ideal for various annotation and labeling use cases, QA workflows, and training computer vision models.
- Specifically designed for computer vision applications, Encord offers native support for a wide array of annotation types, such as bounding box, polygon, polyline, instance segmentation, keypoints, classification, and much more.
- Encord provides use-case-specific annotations, ranging from native DICOM and NIfTI annotations for medical imaging to specialized features catering to SAR (Synthetic Aperture Radar) data in geospatial applications.
- Integrated MLOps workflows for computer vision and machine learning teams — to detect edge cases and gaps in your training data and generate augmented data to improve label quality.
- Streamlined collaboration, annotator management, and quality assurance workflows facilitate precise tracking of annotator performance and elevate label quality.
- Robust security functionality — label audit trails, encryption, FDA, CE Compliance, and HIPAA compliance.
- An advanced Python SDK and API access and effortless export capabilities in JSON and COCO formats enhance flexibility and integration with external systems.
- Auto-find and fix dataset biases and errors like outliers, duplication, and labeling mistakes.
- Integrated tagging for data and labels to create Collections, including outlier tagging.
- Employs quality metrics (data, label, and model) to assess and improve ML pipeline performance across data curation, data labeling, and model training.
iMerit
iMerit is a data labeling service provider known for its annotations and management solutions. Unlike traditional labeling platforms, iMerit offers a service-based approach to data annotation.
iMerit Key Features and Benefits
- Customizable solution for annotation, analysis, categorization, segmentation needs.
- Get insights from metrics such as the annotator's working hours, the number of objects per hour and more.
- iMerit also provides a free trial for it’s users, but has no mention of it’s pricing plan on it’s website.
- iMerit’s user interface may be less intuitive and user-friendly for beginners.
TELUS International
TELUS International, formerly Playment, is a Labelbox alternative that focuses on specialized data labeling services, offering features tailored to specific use cases, ensuring user comfort.
TELUS International Key Features and Benefits
- TELUS International allows the creation of custom data labeling workflows, ensuring that even the most specialized projects can be accommodated.
- The platform has review and feedback loops to maintain the accuracy of annotations.
- CX support in 50+ languages across all traditional and digital channels.
- Integration with other tools and platforms, allows workflow management and collaboration.
- These features allow to accommodate the growing needs of businesses, ensuring that the platform can handle increasing data volumes and complexity.
- There are limited integration options with other third-party software and systems, which may hinder the ability to streamline processes across different platforms.
- Potential challenges in adapting to the training data platform's interface and functionalities, requiring additional training datasets and support for users to fully utilize its capabilities.
CVAT
CVAT, or Computer Vision Annotation Tool, is an open-source platform tailored for data annotation, particularly in the field of computer vision. It stands out as a community-driven solution for data labeling.
CVAT's Key Features and Benefits
- It's a fantastic choice for startups, research projects, and academic initiatives, thanks to its open-source nature.
- CVAT is a cost-effective and highly adaptable alternative to Labelbox
- Being open-source, CVAT encourages community contributions and customization. It's a collaborative tool, making it accessible for a wide range of users, from newbies to pro.
- The process of dataset curation, annotation, training, and dataset improvement is the heart of data-centric AI.
- CVAT has capabilities for bounding boxes, polygons, and keypoint labeling.
- Users can adapt CVAT to their specific needs, through custom plugins, tailored workflows, or support for new data types.
- While CVAT offers a wide range of annotation tools, it does not have all the advanced features that some users may require for their specific annotation tasks.
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Power your AI models with the right data
Automate your data curation, annotation and label validation workflows.
Get startedWritten by
Nikolaj Buhl
Related blogs
Best Image Annotation Tools for Computer Vision [Updated 2024]
If you're looking for an image annotation tool, you have plenty of choices. The market is saturated, making it challenging to find the best tool for your needs. To help you out, we did much of the research for you to streamline your buying process. In this article, you will find a detailed overview of the most popular data annotation tools, including: Encord, Amazon SageMaker Ground Truth, Scale Rapid, Supervisely, CVAT, Labelbox, Playment, Appen, Dataloop, SuperAnnotate, V7, Hive, Label Studio, COCO Annotator, Make Sense, VGG Image Annotator, and, LabelMe Best Image Annotation Tools The sections below give an overview of the key features and user reviews for the above mentioned tools. The summary table below compares all the tools based on supported data types, annotation types, ease of use, and automation. Encord Encord is an end-to-end data development platform with an advanced image annotation tool for complex computer vision and multimodal use cases. The platform offers state-of-the-art model-assisted labeling and customizable workflows to accelerate image annotation projects and build production-ready models. Key Features AI-assisted labeling: Automate 97% of your image annotations with 99% accuracy by leveraging SOTA automated labeling capabilities such as Meta AI’s Segment Anything Model (SAM). Full suite of tools: Encord supports a range of labeling options, such as bounding boxes, rotatable boxes, polygons, polylines, key points, and classifications to support your model requirements. Accelerate with models-in-the-loop: Bring your own model to the Encord platform or leverage one of our Agents to pre-label datasets. Scalability: Encord lets you scale AI projects by supporting extensive datasets of up to 500,000 images. Build balanced datasets: Filter and slice datasets in a consolidated visual explorer and export for labeling in one click. Encord supports deep search, filtering, and metadata analysis. Complex ontologies: Build nested relationship structures in your data schema to improve the quality of your model output. Bulk classification: Leverage natural language or similarity search to select large datasets and label en masse, queue for review to accelerate labeling operations. Build reliable quality control workflows: Build robust workflows with multi-step review stages and consensus benchmarking for quality assurance. Find and fix label errors: Automatically surface labeling errors to shift your attention to the labels impacting model performance. Collaboration: Control user roles with permissions, manage task assignments and infinitely scale your MLOps workflows. Enterprise-grade security as standard: Encord Annotate complies with the General Data Protection Regulation (GDPR), System and Organization Controls 2 (SOC 2), and Health Insurance Portability and Accountability Act (HIPAA) standards while using advanced encryption protocols to ensure data privacy. Integrations: Encord allows you to retain total control of your data. Securely connect your native cloud storage buckets and programmatically control workflows. Advanced Python SDK and API access with easy export into JSON and COCO formats. Integrated Data Labeling Services: Outsource your labeling tasks to an expert workforce of vetted, trained, and specialized annotators. Encord G2 Review Summary Encord has a rating of 4.8/5 based on 60 reviews. Users prefer Encord’s powerful ontology feature, which lets them define rich taxonomy for all data sizes. In addition, the platform’s collaborative features and granular annotation tools help users improve annotation quality. Learn how Encord Annotate helps you create rich ontologies for an efficient labeling process. Curious? Try it out Amazon SageMaker Ground Truth Amazon SageMaker Ground Truth is a human-in-the-loop data labeling platform that offers features to label large datasets. It provides a self-serve and a managed service option to help you streamline your annotation workflow for multiple CV tasks. Amazon SageMaker Ground Truth Key Features Data Generation: The platform offers tools to fine-tune pre-trained models on a few data points to generate synthetic data samples for more diverse training. Model Evaluation: Sagemaker Ground Truth lets you evaluate foundation models based on multiple metrics such as accuracy, relevancy, toxicity, and bias through human feedback. Labeling Templates: It features over thirty labeling templates for multiple CV and NLP tasks, including image classification, object detection, text classification, and named entity recognition (NER). Interactive Dashboards: The tool offers intuitive dashboards and user-friendly interfaces to monitor labeling progress across multiple projects. G2 Review Summary Amazon SageMaker Ground Truth has a rating of 4.1/5 based on 19 reviews. Users like its ease of use and advanced annotation capabilities. However, they feel it is expensive, and tracking labeling performance is challenging. Scale Rapid Scale Rapid is a data and labeling services platform that supports computer vision use cases. It specializes in reinforcement learning with human feedback (RLHF), user experience optimization, large language models (LLMs), and synthetic data. Scale Rapid Key Features Supported Data Types: Scale lets you annotate text, images, video, audio, and point-cloud data. Customizable Workflows: Offers customizable labeling workflows tailored to specific project requirements and use cases. Data labeling services: Provides high-quality data labeling services for various data types, including images, text, audio, and video. Scalability: Capable of handling large-scale annotation projects and accommodating growing datasets and annotation needs. G2 Review Summary Scale Rapid has a rating of 4.4/5 based on 11 reviews. Users say it is easy to learn and does not require complex installation procedures. However, they feel the user interface is clunky and the tool’s pricing is complex. Find out about the top tools that help you perform reinforcement learning with human feedback (RLHF). Supervisely Supervisely is an end-to-end computer vision platform that offers multiple annotation tools for labeling images and videos. It features AI-based labeling that lets users automate labeling workflow through advanced ML models. Supervisely Key Features Versatile Annotation Tool: It supports multiple annotation types, including bounding boxes, polygons, polylines, points, and segmentation masks for precise labeling. Supported Data Types: Supervisely lets you label images, videos, point cloud, and medical image data. Smart Labeling Tools: Feature a class-agnostic smart tool based on customizable neural networks for capturing any object type, depending on your use case. Collaboration: The platform lets you collaborate with team members and assign relevant user roles to track issues and labeling performance. G2 Review Summary Supervisely has a rating of 4.7/5 based on ten reviews. Users like the tool’s integration with multiple apps within the Supervisely ecosystem, giving a smooth user experience. However, the number of options can be overwhelming, and the platform has latency issues. CVAT (Computer Vision Annotation Tool) CVAT is an open-source web-based image annotation tool by Intel. In 2022, CVAT’s data, content, and GitHub repository became a part of OpenCV, where CVAT continues to be open-source. Furthermore, CVAT can also help annotate QR codes within images, facilitating the integration of QR code recognition into computer vision pipelines and applications. CVAT Key Features Manual Annotation Tools: The tool supports various annotation types, including bounding boxes, polygons, polylines, points, and cuboids, catering to diverse annotation needs. Multi-platform Compatibility: Works on multiple operating systems such as Windows, Linux, and macOS, providing flexibility for users. Export Formats: CVAT supports numerous data formats, including JSON, COCO, and Pascal VOC, ensuring annotation compatibility with diverse tools and platforms. Automated Labeling: CVAT supports multiple algorithms, including the Segment Anything Model (SAM), YOLOv3, and Deep Extreme Cut (DEXTR). G2 Review Summary CVAT has a rating of 4.5/5 based on two reviews. Users like that the tool is free to use and requires no configuration and installation process because it is web-based. However, its slow performance and backend server failure are the most significant concerns. Labelbox Labelbox is a US-based data annotation platform founded in 2017 that provides a unified framework for curating and labeling datasets with collaboration and model evaluation tools. Besides a stand-alone image labeling platform, the tool offers managed annotation services with data labeling experts. Labelbox Key Features Data Management: Labelbox offers QA workflows and data annotator performance tracking. Customizable Labeling Interface: It features a user-friendly interface, providing easy-to-navigate editors for specific needs. Automation: Allows integration with AI models for automatic data labeling to accelerate the annotation process. Annotation Capabilities: It supports annotation for multiple data types beyond images, including text, video, audio, geospatial and medical images. G2 Review Summary LabelBox has a rating of 4.7/5 based on 33 reviews. Users find the tool’s data management features helpful. However, they feel that it does not perform well with high-resolution images. Playment Playment is an Indian-based end-to-end data annotation platform founded in 2015 and now operating under Telus’ ownership. It offers managed annotation services by employing computer vision teams to annotate training data for multiple use cases. Playment Key Features Data Labeling Services: Provides high-quality data labeling services for various data types, including images, videos, text, and sensor data. Support: Global workforce of contractors and data labelers. Scalability: Capable of handling large-scale annotation projects and accommodating growing datasets and annotation needs. Audio Labeling Tool: The tool features a speech recognition training platform that can handle over five hundred languages and dialects. G2 Review Summary Playment has a rating of 4.7/5 based on 11 reviews. Users find Playment’s annotation performance fast and accurate. However, they find the tool expensive and that it needs more improvement in automated labeling features. Appen Appen is a data labeling services platform founded in 1996, making it one of the first and oldest solutions in the market, offering data labeling services for various industries. In 2019, it acquired Figure Eight to expand its software capabilities and help businesses train and improve their computer vision models. Appen Key Features Data Labeling Services: Support for multiple annotation types (bounding boxes, polygons, and image segmentation). Data Collection: Data sourcing (pre-labeled datasets), data preparation, and real-world model evaluation. Natural Language Processing: Support for natural language processing (NLP) tasks such as sentiment analysis, entity recognition, and text classification. Image and Video Analysis: Analyzes images and videos for tasks such as object detection, image classification, and video segmentation. G2 Review Summary Appen has a rating of 4.2/5 based on 28 reviews. Users like that the tool is web-based and does not require specific installation procedures. However, the platform’s server crashes frequently, and the support team is slow to respond. Dataloop Dataloop is an Israel-based data labeling platform that provides a comprehensive solution for data management and annotation projects. The tool offers data labeling capabilities across images, text, audio, and video annotation, helping businesses train and improve their machine learning models. Dataloop Key Features Data Annotation: Supports multiple image annotation tasks, including classification, detection, and semantic segmentation. Collaboration Tool: It features tools for real-time collaboration among annotators, project sharing, and version control, allowing for efficient teamwork. Data Management: Offers data management capabilities, including data versioning, tracking, and organization for streamlined workflows. Model Management: Dataloop offers tools to manage different model versions and download SOTA models from the Model Marketplace. G2 Review Summary Dataloop has a rating of 4.4/5 based on 90 reviews. The tool’s plus points include its ease of use and annotation efficiency. However, users find it challenging to learn and face frequent performance issues. SuperAnnotate SuperAnnotate is an end-to-end AI platform that offers tools for data curation and automatic annotation with MLOps functionalities. It also lets you fine-tune LLMs using annotated data and RLHF. SuperAnnotate Key Features Multi-Data Type Support: Versatile annotation features for labeling videos, text, audio, and image data. AI Assistance: Integrates AI-assisted annotation to accelerate the labeling process and improve efficiency. Customization: Provides customizable annotation interfaces and workflows to tailor annotation tasks to specific project requirements. Export Formats: SuperAnnotate supports multiple data formats, including popular ones like JSON, COCO, and Pascal VOC. G2 Review Summary SuperAnnotate has a rating of 4.9/5 based on 137 reviews. Users find the tool’s feature set comprehensive and the interface intuitive. However, there have been complaints regarding its custom workflow setup and high price. V7 Labs V7 is a UK-based data annotation platform founded in 2018. The company enables teams to annotate image and video data using automated pipelines and custom workflows. The platform also offers model and data management tools to help users build high-quality training data for scalable AI projects. V7 Key Features Collaboration Capabilities: Project management and automation workflow functionality, with real-time collaboration and tagging. Data Management: The tool offers data management features, including functionalities to filter and sort data. It also helps organize and manage data classes at team and dataset levels. Auto-Annotate: Features auto-annotation that lets you use deep learning models to create pixel-perfect polygon masks. Auto-Track: V7 offers an auto-track feature for object tracking and instance segmentation in long videos. G2 Review Summary V7 has a rating of 4.8/5 based on 52 reviews. Users find its automation and collaboration features significantly helpful. However, they feel it lacks file manipulation options, and its sorting and filtering features do not perform well with large files. Hive Hive is a content-moderation platform that offers deep learning models to highlight harmful and explicit content in images, videos, text, and audio. It also features search and generative APIs to visualize similarities between images and videos and generate images based on textual prompts. Hive AI Key Features Ease of use: Hive offers an intuitive interface with multiple in-built image and text classification models. Embeddings: The platform lets you quickly create text embeddings to build retrieval augmented generation (RAG)-based LLMs. Search: Hive offers versatile web search functionality. You can use image prompts to retrieve relevant links to similar images. Generative Artificial Intelligence (Gen AI): Hive features APIs to generate text, images, and videos based on textual prompts. G2 Review Summary Hive has a rating of 4.6/5 based on 528 reviews. Users find its project management and collaboration features helpful. However, the interface is challenging to navigate and has a few glitches, which makes it complex to operate. Label Studio Label Studio is a popular open-source data labeling platform for annotating various data types, including images, text, audio, and video. It supports collaborative labeling, custom labeling interfaces, and integration with machine learning (ML) pipelines for data annotation tasks. Label Studio Key Features Customizable Labeling Interfaces: Label Studio lets you label data through flexible configurations that allow you to tailor annotation interfaces to specific tasks. Collaboration Tools: Real-time annotation and project-sharing capabilities for seamless collaboration among annotators. Export Formats: Label Studio supports multiple data formats, including JSON, CSV, TSV, and VOC XML like Pascal VOC, facilitating integration and annotation from diverse sources for machine learning tasks. ML Pipelines: Label Studio lets you connect the model development pipeline with the data labeling project. The method allows you to use ML models to predict labels, evaluate model performance, and perform human-in-the-loop labeling. G2 Review Summary G2 review not available. COCO Annotator COCO Annotator is a web-based labeling tool by Justin Brooks that is under the MIT license. The tool helps streamline the process of annotating images for object recognition, localization, and key point detection models. It also offers a range of features that cater to the diverse needs of machine learning practitioners, data scientists, and researchers. COCO Annotator Key Features Image Annotation: Supports annotation of images for object detection, instance segmentation, keypoint detection, and captioning tasks. Export Formats: The tool exports and stores annotations in the COCO format to facilitate large-scale object detection. Automation: The tool makes annotating an image easier by incorporating semi-trained models. It also provides access to advanced selection tools, including the Mask Region-based Convolutional Neural Network (MaskRCNN), Magic Wand, and Deep Extreme Cut (DEXTR) frameworks. Metadata Management: Users can create custom metadata for each instance or object. G2 Review Summary G2 review not available. Make Sense Make Sense AI is a user-friendly open-source annotation tool available under the GPLv3 license. It is accessible through a web browser and does not require advanced installations. The tool simplifies the annotation process for multiple image types. Make Sense Key Features Open Sourced: Make Sense AI stands out as an open-source tool, freely available under the GPLv3 license, fostering collaboration and community engagement for its ongoing development. Accessibility: It ensures web-based accessibility, operating seamlessly in a web browser without complex installations, promoting ease of use across various devices. Export Formats: It facilitates exporting annotations in multiple formats (YOLO, VOC XML, VGG JSON, and CSV), ensuring compatibility with diverse machine learning algorithms. Supported Annotation Types: The tool supports rectangles, lines, points, and polygons. G2 Review Summary G2 review not available. VGG Image Annotator VGG Image Annotator (VIA) is a versatile open-source tool by the Visual Geometry Group (VGG) for manually annotating image and video data. Released under the permissive BSD-2 clause license, VIA serves the needs of academic and commercial users, offering a lightweight and accessible solution for annotation tasks. VIA Key Features Lightweight and User-Friendly: VIA is a lightweight, self-contained annotation tool that uses HTML, Javascript, and CSS without external libraries. Offline Capability: The tool works offline, providing a full application experience within a single HTML file of less than 200 KB. Audio and Video Annotation: In addition to images, the tool lets users define temporal segments in audio and video data with textual descriptions. Supported Annotation Types: The tool allows you to draw rectangles, circles, ellipses, polygons, points, and polylines. G2 Review Summary G2 review not available. LabelMe LabelMe is an open-source web-based tool by the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) that allows users to label and annotate images for computer vision research. It provides a user-friendly interface for drawing bounding boxes, polygons, and semantic segmentation masks to label objects within images. LabelMe Key Features Web-Based: Accessible through a web-based interface, allowing you to perform annotation tasks in any modern web browser without requiring software installation. Supported Data Types: The tool supports image and video annotation. Supported Annotation Types: LabelMe lets you draw polygons, rectangles, circles, lines, and points. Export Format: It lets you export annotation in VOC and COCO format for semantic and instance segmentation. G2 Review Summary G2 review not available. Key Takeaways: Image Annotation Tools in 2024 As data volume and variety increase, businesses must invest in a suitable and reliable annotation tool to build high-quality datasets for training models. Below are a few key points regarding the top image annotation tools and tips for selecting an appropriate solution. Best Annotation Tools in 2024: Encord, Amazon Sagemaker Ground Truth, and Scale Rapid are the top annotation tools in 2024. Ease-of-use: Most G2 reviews highlight issues with the user interface. You should ensure that you select a tool that offers intuitive navigation and labeling features. Automation: Select a platform that offers state-of-the-art automation features, including pre-trained models and smart labeling tools. Open-source vs. Paid Platforms: While open-source tools offer a cost-effective solution, they have limited functionality. Paid tools provide a rich feature set with robust customer support to help you annotate multiple data types. So, streamline your CV operations with the annotation tool that best suits your needs.
Mar 26 2024
10 M
Top 8 Video Annotation Tools for Computer Vision
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 Best Image Annotation Tools blog 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.
May 11 2023
4 M
Top Tools for Outlier Detection in Computer Vision
Data contains hidden insights that completely alter how we make business decisions. However, data often consists of abnormal instances, known as outliers, that can distort the outcome of data processing and analysis. Moreover, machine learning (ML) models trained using data with outliers may have suboptimal predictive performance. Hence, outlier detection is a crucial step in any data pipeline. Here's the catch: manually identifying data outliers is difficult and time-consuming, especially for large datasets. As a result, data scientists and artificial intelligence (AI) practitioners employ outlier detection tools to quickly identify outliers and streamline their data processing and ML pipelines. In this guide, we’ll explore outlier detection techniques and list the top tools that can be utilized for this purpose. These include: Encord Active Lightly Aquarium Voxel Deepchecks Arize Outlier Detection: Types & Methods Outliers are data points with extreme values that are at disproportionately large distances from the normal distribution of the dataset. They represent an abnormal pattern compared to the regular data points. They can occur for various reasons, including data entry and label errors, measurement discrepancies, missing values, and rare events. There are three main types of outliers: Global or Point Outliers: Individual data points that deviate significantly from the normal distribution of the dataset. Contextual Outliers: Data points with abnormal distances within a specific context or subset of the data. Collective Outliers: Groups or subsets of data that exhibit unusual patterns compared to the entire dataset. Outliers are also classified based on the number of variables. These are: Univariate Outliers: Data points of a single variable that are distant from regular observations. Multivariate Outliers: A combination of extreme data values on two or more variables. Illustration of outliers in 2D data Now, let’s explore some common outlier detection methods that AI practitioners use: Z-score Method This method identifies outliers based on the number of standard deviations from the mean. In other words, the z-score is a statistical measurement that determines how distant a data point is from its distribution. Typically, a data point with a Z-score beyond +3 or -3 is considered an outlier. The Z-score results are best visualized with histograms and scatter plots. Clustering Method This method identifies various data clusters in the dataset distribution using techniques like: K-means clustering, a technique that creates clusters of similar data points, where each cluster has a centroid (center points or cluster representatives within a dataset), and data points within one cluster are dissimilar to the data points in another cluster. Density-based spatial clustering of applications with noise (DBSCAN) to detect data points that are in areas of low density (where the nearest clusters are far away) In such methods, outliers are identified by calculating the distance between each data point and the centroid, and data points that are farthest from the cluster centers are typically categorized as outliers. The clustering results are best visualized on scatter plots. Interquartile range (IQR) Method This method identifies outliers based on their position in relation to the data distribution's percentiles. The IQR is calculated as the difference between the third quartile (Q3) and first quartile (Q1) in a rank-ordered portion of data. Typically, an outlier is identified when a data point is more than 1.5 times the IQR distance from either the lower (Q1) or upper quartile (Q3). The IQR method results are best visualized with box plots. Many outlier detection tools use similar or more advanced methods to quickly find anomalies in large datasets. And there are many out there. How can you pick the one that best suits your requirements? Let’s compare our curated list of top outlier detection tools to help you find the right one. Our comparison will be based on key factors, including outlier detection features, support for data types, customer support, and pricing. Encord Active Encord Active is a powerful active learning toolkit for advanced error analysis for computer vision data to accelerate model development. Encord Active dashboard Benefits & Key Features Surface and prioritize the most valuable data for labeling Search and curate data across images, videos, DICOM files, labels, and metadata using natural language search Auto-find and fix dataset biases and errors like outliers, duplication, and labeling mistakes Find machine learning model failure modes and edge cases Employs precomputed interquartile ranges to process visual data and uncover anomalies Integrated tagging for data and labels, including outlier tagging Export, re-label, augment, review, or delete outliers from your dataset Employs quality metrics (data, label, and model) to evaluate and improve ML pipeline performance across several dimensions, like data collection, data labeling, and model training. Integrated filtering based on quality metrics Supports data types like jpg, png, tiff, and mp4 Supports label types like bounding boxes, polygons, segmentation, and classification Advanced Python SDK and API access to programmatically access projects, datasets, and labels Provides interactive visualizations, enabling users to analyze detected outliers comprehensively Offers collaborative workflows, enabling efficient teamwork and improved annotation quality Best for Teams Who Are looking to upgrade from in-house solutions and require a reliable, secure, and collaborative platform to scale their anomaly detection workflows effectively. Need a suite of powerful tools to work on complex computer vision use cases across verticals like smart cities, AR/VR, autonomous transportation, and sports analytics. Haven't found an anomaly detection platform that aligns perfectly with their specific use case requirements Read our step-by-step guide to Improving Training Data with Outlier Detection with Encord Pricing There are two core offerings: a free, open-source version, and a team plan which requires a support contact. Lightly Lightly is a data curation software for computer vision that offers improved model accuracy by utilizing active learning to find clusters or subsets of high-impact data within your training dataset. Lightly dashboard Benefits & Key Features Data selection is done via active and self-supervised learning algorithms based on three input types: embeddings, metadata, and predictions. Automates image and video data curation at scale to mitigate dataset bias Built-in capability to check for corrupt images or broken frames Data drift and model drift monitoring Python SDK to integrate with other frameworks and your existing ML stack using scripts LightlyWorker tool – a docker container to leverage GPU capabilities Best for Teams Who Require GPU capabilities to curate large-scale vision datasets, including special data types like LIDAR, RADAR, and medical. Want a collaborative platform for dataset sharing Pricing Lightly offers free community and paid versions for teams and custom plans. Aquarium Aquarium is an ML data operations platform that allows data management with a focus on improving training data. It utilizes embedding technology to surface problems in model performance. Aquarium dashboard Users can upload streaming datasets into Aquarium's data operations platform. It retains the history of changes, enabling users to analyze the evolution of the dataset over time and gain insights. Benefits & Key Features Generate, process, and query embeddings to find clusters of high-quality data from unlabeled datasets Allows for a variety of data to be curated, including images, 3D data, audio, and text Integrates with data labeling suppliers and ML tools like TensorFlow, Keras, Google Cloud, Azure, and AWS Inspects data and labels using visualization to find errors and bad data quickly Automatically analyze and calculate model metrics to identify erroneous data points Community and shared Slack channel support, as well as solution engineering assistance Best for Teams Who Require integration of vendor systems with a data operations platform enabling efficient data flow Need ML team collaboration on data curation and evaluation tasks Interested in learning more about the role of data operations? Read our comprehensive Best Practice Guide for Computer Vision Data Operations Teams. Pricing Aquarium offers a free tier for a single user. They also offer team, business, and enterprise tiers for multiple users. Voxel51 Voxel51 is an open-source toolkit for curating high-quality datasets and building computer vision production workflows. FiftyOne dashboard Benefits & Key Features Integrates with ML tools to annotate, train, filter, and evaluate models Identifies your model’s failure modes Removes redundant images from training data Finds and corrects label mistakes to curate higher-quality datasets Dedicated slack channel for customer support Best for Teams Who Want to start with open-source tooling Require a graphical user interface that enables them to visualize, browse, and interact directly with their datasets Pricing There are two core offerings: FiftyOne, a free, open-source platform, and FiftyOne Teams plan, which requires a support contact. Deepchecks Deepchecks is an ML platform and Python library for deep learning model monitoring and debugging. It offers validation of machine learning algorithms and data with minimal effort in the research and production phases. Deepchecks dashboard The Deepchecks tool utilizes the LoOP algorithm, a method for detecting outliers in a dataset across multiple variables by comparing the density in the area of a sample with the densities in the areas of its nearest neighbors. Benefits & Key Features Utilizes Gower distance with LoOP algorithm to identify outliers Real-time monitoring of model performance and metrics (such as label drift) Provides Role-Based Access Control (RBAC) Prioritizes data privacy by encrypting data during transit and storage Slack community and Enterprise support for users Best for Teams Who Are required to monitor model performance and find and resolve production issues Deal with sensitive data and value a secure deployment Want to learn how to handle data pipelines at scale? Read our explanatory post on How Automated Data Labeling is Solving Large-Scale Challenges. Pricing Deepchecks offers open-source and paid plans depending on the team’s security and support requirements. Arize Arize is an ML observability platform to help data scientists and ML engineers detect model issues, fix their underlying causes, and improve model performance. It allows teams to monitor, detect anomalies, and perform root cause analysis for model improvement. Arize dashboard It has a central inference store and comprehensive datasets indexing capabilities across environments (training, validation, and production), providing insights and making it easier to troubleshoot and optimize model performance. Benefits & Key Features Detect model issues in production Uses Vector Similarity Search to find problematic clusters containing outliers to fine-tune the model with high-quality data Automatic generation and sorting of clusters with semantically similar data points Best for Teams Who: Require real-time model monitoring for immediate feedback on model prediction and forecasting outcomes Pricing Arize offers a free tier for individuals and paid plans for small and global teams. What Should You Look For in an Outlier Detection Tool? Outlier detection is a crucial step in machine learning for ensuring data quality, accurate statistics, and reliable model performance. Various tools utilize different outlier detection algorithms and methods, so selecting the best tool for your dataset is essential. Consider the following factors when selecting an outlier detection tool: Ease of Use: Choose a user-friendly outlier identification solution that allows data scientists to focus on insights and analysis rather than a complex setup. Scalability: Select a solution that can efficiently handle enormous datasets, enabling real-time detection. Flexibility: Choose a platform that provides customizable options tailored to your unique data and outlier analysis use cases. This is essential for optimal performance. Visualizations: Select a platform that delivers clear and interactive visualizations to help you easily understand and analyze outlier data. Integration: Choose a tool that connects effortlessly to your existing data operations system, making it simple to incorporate outlier identification into your data processing and evaluation pipeline.
Aug 01 2023
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