# Best Data Labeling Software ## How Many Data Labeling Software Products Does G2 Track? **Total Products under this Category:** 116 ### Category Stats (Jul 2026) - **Average Rating:** 4.49/5 (↓0.02 vs Jun 2026) The average rating of products in this category, based on all submitted ratings - **Top Trending Product:** Datature (+0.08%) - Among all products in this category, Datature recorded the largest rating increase compared to last month _Last updated: July 31, 2026_ ## How Does G2 Rank Data Labeling Software Products? **Why You Can Trust G2's Software Rankings:** - 30 Analysts and Data Experts - 1,800+ Authentic Reviews - 116+ Products - Unbiased Rankings G2's software rankings are built on verified user reviews, rigorous moderation, and a consistent research methodology maintained by a team of analysts and data experts. Each product is measured using the same transparent criteria, with no paid placement or vendor influence. While reviews reflect real user experiences, which can be subjective, they offer valuable insight into how software performs in the hands of professionals. Together, these inputs power the G2 Score, a standardized way to compare tools within every category. ## G2 Grid® for Data Labeling Software ![G2 Grid® for Data Labeling Software plotting products by satisfaction and market presence](https://www.g2.com/categories/data-labeling/grids.png?focus%5B%5D=125020&focus%5B%5D=128515&focus%5B%5D=168222&focus%5B%5D=78925&focus%5B%5D=87452&focus%5B%5D=142739&focus%5B%5D=126287&focus%5B%5D=1563734) Highlighted products: Roboflow, SuperAnnotate, Encord, Labelbox, Amazon Sagemaker Ground Truth, Sama, V7 Darwin, and Outlier AI. Underlying data: [Grid® JSON](https://www.g2.com/categories/data-labeling/grids.json?focus%5B%5D=roboflow&focus%5B%5D=superannotate&focus%5B%5D=encord&focus%5B%5D=labelbox&focus%5B%5D=amazon-sagemaker-ground-truth&focus%5B%5D=sama&focus%5B%5D=v7-darwin&focus%5B%5D=outlier-ai-outlier-ai) **Sponsored** ### Gemini Enterprise Agent Platform Google Cloud's comprehensive platform for developers to build, scale, govern and optimize agents and models. It's a single destination for technical teams to build agents that can transform enterprise applications and workflows into powerful agentic systems. [Visit website](https://www.g2.com/external_clickthroughs/record?secure%5Bad_program%5D=ppc&secure%5Bad_slot%5D=category_product_list_llm&secure%5Bcategory_id%5D=2361&secure%5Bchosen_at%5D=2026-08-01T00%3A32%3A12Z&secure%5Bdisplayable_resource_id%5D=2361&secure%5Bdisplayable_resource_type%5D=Category&secure%5Bmedium%5D=sponsored&secure%5Bplacement_reason%5D=page_category&secure%5Bplacement_resource_ids%5D%5B%5D=2361&secure%5Bprioritized%5D=false&secure%5Bproduct_id%5D=21469&secure%5Bresource_id%5D=2361&secure%5Bresource_type%5D=Category&secure%5Bsource_type%5D=category_page&secure%5Bsource_url%5D=https%3A%2F%2Fwww.g2.com%2Fcategories%2Fdata-labeling%3Fopen_modal_url%3D%252Fproducts%252Fai-data-collection-company%252Fwishlists%253Fhost_path%253D%25252Fcategories%25252Fdata-labeling%2526source%253Dcategory&secure%5Btoken%5D=d6092772de551ece63d7b75d299f2adc8a67a65c89e6c0cd923ad83af406f5e1&secure%5Burl%5D=https%3A%2F%2Fcloud.google.com%2Fproducts%2Fgemini-enterprise-agent-platform%3Futm_source%3DG2%26utm_medium%3Ddisplay%26utm_campaign%3DCloud-SS-DR-GCP-1713658-GCP-DR-NA-US-en-G2-Display-Banner-All-%2525epid%21-%2525ecid%21-geap%26utm_content%3D%257Bdevice%257D-%257Badgroupid%257D-%257Bnetwork%257D-%257Btargetid%257D-%257Bloc_physical_ms%257D-%257Bcampaignid%257D&secure%5Burl_type%5D=custom_url) ### [SuperAnnotate](https://www.g2.com/products/superannotate/reviews) SuperAnnotate bridges the gap between cutting-edge AI innovation and the high-quality human data that powers it - helping advanced AI teams build more intelligent models. With a global network of thousands of rigorously vetted experts, ethical and scalable managed operations, precise talent matching, and purpose‑built technology, SuperAnnotate delivers full project visibility and unmatched data quality. SuperAnnotate powers complex annotation, evaluation, and reinforcement learning workflows to build, evaluate and align frontier AI. Trusted by innovators like Databricks, IBM and ServiceNow - and backed by NVIDIA, Dell Technologies Capital, Databricks Ventures, Cox Enterprises, and Lionel Messi’s Play Time VC - SuperAnnotate enables the world’s top AI teams to build responsible and state‑of‑the‑art models with human data. **Average Rating:** 4.8/5.0 **Total Reviews:** 353 #### How Do G2 Users Rate SuperAnnotate? - **Labeler Quality:** 9.6/10 (Category avg: 8.9/10) - **Object Detection:** 9.4/10 (Category avg: 8.9/10) - **Data Types:** 9.5/10 (Category avg: 8.8/10) - **Ease of Use:** 9.5/10 (Category avg: 8.8/10) #### Who Is the Company Behind SuperAnnotate? - **Seller:** [SuperAnnotate](https://www.g2.com/sellers/superannotate) - **Company Website:** superannotate.com - **Year Founded:** 2018 - **HQ Location:** San Francisco, CA - **Twitter:** @superannotate 720 Twitter followers - **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=ed4d3a394b0ca2eaac6c63041f9bd1bf14ee26538d356cd977a2b0f50c15f4d1&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F18999422%2F&secure%5Burl_type%5D=linkedin_company_website) 361 employees on LinkedIn® #### Who Uses This Product? - **Who Uses This:** Student, Data Trainer - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 57% Small, 23% Medium #### What Do G2 Reviewers Say About SuperAnnotate? _AI-generated summary from verified user reviews_ ##### Pros - Users appreciate the **intuitive interface** of SuperAnnotate, making large-scale annotation projects easy to manage and efficient. - Users appreciate the **user-friendly interface** of SuperAnnotate, which enhances the efficiency and accuracy of their annotation tasks. - Users value the **annotation efficiency** of SuperAnnotate, enabling quick, consistent, and high-quality annotations across diverse projects. - Users commend the **efficiency** of SuperAnnotate, appreciating the time saved and the streamlined annotation process. - Users value SuperAnnotate for its **high-quality annotations** , ensuring consistent and efficient collaboration and management of projects. ##### Cons - Users notice **performance issues** with SuperAnnotate, particularly related to loading times and occasional technical glitches. - Users experience **slow performance** with SuperAnnotate, particularly during image cropping and handling large projects. - Users find the **difficult learning** curve for advanced features challenging, impacting their overall experience with SuperAnnotate. - Users find the **complexity of the platform** daunting, particularly for new users navigating advanced features. - Users find a **lack of guidance** in SuperAnnotate, making it challenging for newcomers to navigate advanced features effectively. #### What Are Recent G2 Reviews of SuperAnnotate? **["Easy-to-Use Data Organization and Powerful Image-Splitting Tools"](https://www.g2.com/survey_responses/superannotate-review-13146852)** **Rating:** 5.0/5.0 stars _— Doniaa K._ [Read full review](https://www.g2.com/survey_responses/superannotate-review-13146852) **["Streamlines Annotation with an Easy Setup and Strong Support"](https://www.g2.com/survey_responses/superannotate-review-12584940)** **Rating:** 4.0/5.0 stars _— Nada A._ [Read full review](https://www.g2.com/survey_responses/superannotate-review-12584940) #### What Are G2 Users Discussing About SuperAnnotate? - [What is your experience with SuperAnnotate for data annotation, and what would you like to see improved?](https://www.g2.com/discussions/what-is-your-experience-with-superannotate-for-data-annotation-and-what-would-you-like-to-see-improved) - 1 comment - [How do I annotate an image in OpenCV?](https://www.g2.com/discussions/how-do-i-annotate-an-image-in-opencv) - [Is SuperAnnotate free?](https://www.g2.com/discussions/is-superannotate-free) - [How do you use SuperAnnotate?](https://www.g2.com/discussions/how-do-you-use-superannotate) - [What is SuperAnnotate?](https://www.g2.com/discussions/what-is-superannotate) - 1 comment, 2 upvotes ### [Roboflow](https://www.g2.com/fr/products/roboflow/reviews) Roboflow a tout ce dont vous avez besoin pour créer et déployer des applications de vision par ordinateur. Plus de 1 000 000 utilisateurs d'entreprises de toutes tailles — des startups aux entreprises publiques — utilisent la plateforme de bout en bout de l'entreprise pour la collecte, l'organisation, l'annotation, le prétraitement, l'entraînement de modèles et le déploiement d'images et de vidéos. Roboflow fournit des outils pour chaque étape du cycle de vie du déploiement de la vision par ordinateur et s'intègre à vos solutions existantes afin que vous puissiez adapter votre pipeline pour répondre à vos besoins. **Average Rating:** 4.7/5.0 **Total Reviews:** 155 #### How Do G2 Users Rate Roboflow? - **Qualité de l’étiqueteuse:** 9.0/10 (Category avg: 8.9/10) - **Détection d’objets:** 9.1/10 (Category avg: 8.9/10) - **Types de données:** 8.7/10 (Category avg: 8.8/10) - **Facilité d’utilisation:** 9.3/10 (Category avg: 8.8/10) #### Who Is the Company Behind Roboflow? - **Vendeur:** [Roboflow](https://www.g2.com/fr/sellers/roboflow) - **Site Web de l'entreprise:** roboflow.com - **Année de fondation:** 2019 - **Emplacement du siège social:** Remote, US - **Twitter:** @roboflow 13,577 abonnés Twitter - **Page LinkedIn®:** [www.linkedin.com](https://www.g2.com/fr/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=660f87d85fdd82e0f1cecfe2354a16103bb5a6f5508134496575ec655201678c&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F36096640&secure%5Burl_type%5D=linkedin_company_website) 137 employés sur LinkedIn® #### Who Uses This Product? - **Who Uses This:** Fondateur, Chercheur - **Top Industries:** Logiciels informatiques, Recherche - **Company Size:** 78% Small, 14% Medium #### What Do G2 Reviewers Say About Roboflow? _AI-generated summary from verified user reviews_ ##### Pros - Les utilisateurs apprécient l' **interface intuitive** de Roboflow, permettant une annotation efficace et une collaboration fluide pour les projets de vision par ordinateur. - Les utilisateurs apprécient l' **efficacité** de Roboflow, louant sa gestion de données rationalisée qui permet de gagner du temps et de réduire les erreurs. - Les utilisateurs apprécient l' **efficacité de l'annotation** de Roboflow, profitant d'une gestion de jeu de données rationalisée qui permet de gagner du temps et de réduire les erreurs. - Les utilisateurs apprécient le **processus de labellisation des données facile** dans Roboflow, qui simplifie l'annotation et améliore la collaboration en équipe. - Les utilisateurs apprécient les **fonctionnalités puissantes et polyvalentes** de Roboflow, qui améliorent les projets académiques et les tâches de vision par ordinateur. ##### Cons - Les utilisateurs trouvent Roboflow **cher** , surtout pour les étudiants, car les fonctionnalités clés nécessitent des plans payants pour la confidentialité et la personnalisation. - Les utilisateurs notent un **manque de fonctionnalités** pour l'analyse avancée et la personnalisation sur les plans de niveau inférieur dans Roboflow. - Les utilisateurs trouvent une **fonctionnalité limitée** dans Roboflow, rencontrant des défis tels que des restrictions de fonctionnalités et un manque de flexibilité dans les tâches avancées. - Les utilisateurs rencontrent des **problèmes d'annotation** avec Roboflow, nécessitant souvent des ajustements manuels étendus pour l'exactitude et l'efficacité. - Les utilisateurs trouvent la gestion de **l'étiquetage inefficace** fastidieuse, nécessitant une organisation manuelle et manquant de raccourcis pour une complétion d'annotation plus fluide. #### What Are Recent G2 Reviews of Roboflow? **["Accélérez notre recherche agri-CV"](https://www.g2.com/fr/survey_responses/roboflow-review-12692685)** **Rating:** 5.0/5.0 stars _— Alexey K._ [Read full review](https://www.g2.com/fr/survey_responses/roboflow-review-12692685) **["Roboflow rend les projets de vision par ordinateur faciles à construire, entraîner et déployer."](https://www.g2.com/fr/survey_responses/roboflow-review-12984362)** **Rating:** 5.0/5.0 stars _— noah r._ [Read full review](https://www.g2.com/fr/survey_responses/roboflow-review-12984362) ### [Encord](https://www.g2.com/products/encord/reviews) Encord is the universal data layer for AI. The platform helps AI teams train and run their models with the right data - managing, curating, annotating, and aligning data across the full AI lifecycle. Encord works with over 300 leading AI teams, including Woven by Toyota, Zipline, AXA, and Flock Safety. Confidentially build production AI with rich multimodal data. Encord is SOC 2, AICPA SOC, HIPAA, and GDPR compliant. **Average Rating:** 4.8/5.0 **Total Reviews:** 65 #### How Do G2 Users Rate Encord? - **Labeler Quality:** 9.4/10 (Category avg: 8.9/10) - **Object Detection:** 9.3/10 (Category avg: 8.9/10) - **Data Types:** 9.7/10 (Category avg: 8.8/10) - **Ease of Use:** 9.5/10 (Category avg: 8.8/10) #### Who Is the Company Behind Encord? - **Seller:** [Encord](https://www.g2.com/sellers/encord) - **Year Founded:** 2020 - **HQ Location:** San Francisco, US - **Twitter:** @encord\_team 1,014 Twitter followers - **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=b5b79c871099fa074d165dec60c3345148d0ae005f29a546864b838e8a7500a8&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F69557125&secure%5Burl_type%5D=linkedin_company_website) 195 employees on LinkedIn® #### Who Uses This Product? - **Top Industries:** Computer Software, Hospital & Health Care - **Company Size:** 51% Small, 40% Medium #### What Do G2 Reviewers Say About Encord? _AI-generated summary from verified user reviews_ ##### Pros - Users commend the **responsive customer support** from Encord, ensuring quick assistance during their project needs. - Users praise Encord for its **annotation efficiency** , streamlining workflows and enhancing speed in data curation tasks. - Users value the **intuitive interface and seamless integration** of Encord's annotation tools for efficient data workflow. - Users appreciate the **efficiency** of Encord, highlighting smooth workflows and quick integration that enhances their productivity. - Users value Encord's **intuitive interface and seamless integration** , enhancing their workflows with efficient data curation and support. ##### Cons - Users find **complex automation** challenging, though the supportive team helps navigate the intricacies of custom workflows. - Users find it challenging to stay current due to **frequent updates** , but support from the team is helpful. - Users find the **lack of guidance** challenging due to rapid feature updates, despite helpful customer support. #### What Are Recent G2 Reviews of Encord? **["Strong video labeling platform with excellent support"](https://www.g2.com/survey_responses/encord-review-12281672)** **Rating:** 5.0/5.0 stars _— Angela S._ [Read full review](https://www.g2.com/survey_responses/encord-review-12281672) **["Built for fast model development cycles"](https://www.g2.com/survey_responses/encord-review-12219596)** **Rating:** 5.0/5.0 stars _— Brian E._ [Read full review](https://www.g2.com/survey_responses/encord-review-12219596) ### [Labelbox](https://www.g2.com/fr/products/labelbox/reviews) Labelbox est la principale plateforme d'IA centrée sur les données pour créer des applications intelligentes. Les équipes cherchant à tirer parti des dernières avancées en matière d'IA générative et de LLM utilisent la plateforme Labelbox pour injecter ces systèmes avec le bon degré de supervision humaine et d'automatisation. Qu'elles construisent des produits d'IA avec des modèles personnalisés ou de base, ou qu'elles utilisent l'IA pour automatiser des tâches de données ou trouver des insights commerciaux, Labelbox permet aux équipes de le faire efficacement et rapidement. La plateforme est utilisée par des entreprises du Fortune 500 telles que Walmart, P&G, Genentech et Adobe, ainsi que par des centaines d'équipes d'IA de premier plan. Labelbox est soutenu par des investisseurs de premier plan, y compris SoftBank, Andreessen Horowitz, B Capital, Gradient Ventures (le fonds axé sur l'IA de Google) et Databricks Ventures. **Average Rating:** 4.5/5.0 **Total Reviews:** 48 #### How Do G2 Users Rate Labelbox? - **Qualité de l’étiqueteuse:** 9.1/10 (Category avg: 8.9/10) - **Détection d’objets:** 8.6/10 (Category avg: 8.9/10) - **Types de données:** 8.8/10 (Category avg: 8.8/10) - **Facilité d’utilisation:** 9.0/10 (Category avg: 8.8/10) #### Who Is the Company Behind Labelbox? - **Vendeur:** [Labelbox](https://www.g2.com/fr/sellers/labelbox) - **Année de fondation:** 2018 - **Emplacement du siège social:** San Francisco, California - **Twitter:** @labelbox 3,489 abonnés Twitter - **Page LinkedIn®:** [www.linkedin.com](https://www.g2.com/fr/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=46b35e20f0b8a1bbee233e1f6d5a6d1a72e4e49ac4f1901248ae276388e42159&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Flabelbox%2F&secure%5Burl_type%5D=linkedin_company_website) 469 employés sur LinkedIn® #### Who Uses This Product? - **Top Industries:** Logiciels informatiques, Technologie de l'information et services - **Company Size:** 46% Small, 38% Medium #### What Do G2 Reviewers Say About Labelbox? _AI-generated summary from verified user reviews_ ##### Pros - Les utilisateurs louent la **facilité d'utilisation** de Labelbox, soulignant sa configuration simple et ses fonctionnalités accessibles pour la gestion de projet. - Les utilisateurs louent le **processus de labellisation des données facile et rapide** de Labelbox, améliorant ainsi le flux de travail et la qualité des données de manière efficace. - Les utilisateurs apprécient les **capacités de gestion de projet efficaces** de Labelbox, rationalisant les tâches et améliorant l'organisation globale. - Les utilisateurs apprécient les **capacités d'IA** de Labelbox pour simplifier l'étiquetage des données et améliorer efficacement la précision des modèles. - Les utilisateurs trouvent des **intégrations faciles** avec Labelbox, améliorant leur expérience grâce à une interface conviviale et une configuration rapide. ##### Cons - Les utilisateurs expriment leur frustration face au **manque de fonctionnalités** , se sentant limités par l'incapacité de personnaliser et d'augmenter la disponibilité des tâches. - Les utilisateurs rencontrent une **performance lente** avec Labelbox, surtout lorsqu'ils travaillent avec de grands ensembles de données et des visualisations de sortie. - Les utilisateurs trouvent que Labelbox a **un apprentissage difficile** en raison de sa complexité et de son traitement lent avec de grands ensembles de données. - Les utilisateurs trouvent Labelbox **cher** et expriment des préoccupations concernant le coût élevé, surtout pour les petits utilisateurs. - Les utilisateurs expriment leur frustration face à la **lenteur du traitement** de Labelbox, retardant le démarrage des projets et l'expérience globale. #### What Are Recent G2 Reviews of Labelbox? **["Formation LLM à son meilleur !"](https://www.g2.com/fr/survey_responses/labelbox-review-11265400)** **Rating:** 4.5/5.0 stars _— Staci T._ [Read full review](https://www.g2.com/fr/survey_responses/labelbox-review-11265400) **["Interface professionnelle, configuration simple, nécessite une mise à jour des données"](https://www.g2.com/fr/survey_responses/labelbox-review-12625977)** **Rating:** 4.0/5.0 stars _— Ashish S._ [Read full review](https://www.g2.com/fr/survey_responses/labelbox-review-12625977) #### What Are G2 Users Discussing About Labelbox? - [How do I create a labeled dataset?](https://www.g2.com/fr/discussions/how-do-i-create-a-labeled-dataset) - [What is label tool?](https://www.g2.com/fr/discussions/what-is-label-tool) - [How do I download Labelbox?](https://www.g2.com/fr/discussions/how-do-i-download-labelbox) - 2 comments - [Is Labelbox open source?](https://www.g2.com/fr/discussions/is-labelbox-open-source) ## FAQs About Data Labeling Software Generated using AI Last updated: June 3, 2026 ### Which Data Labeling platforms support computer vision models using bounding boxes and point cloud annotation Based on G2 reviews, these products are the clearest fits for image and point cloud annotation needs. - [Taskmonk](https://www.g2.com/products/taskmonk) — LiDAR projects with quality checks. - [SuperAnnotate](https://www.g2.com/products/superannotate) — bounding boxes, segmentation, review workflows. - [Segments.ai](https://www.g2.com/products/segments-ai) — multi-sensor and point cloud labeling. - [Roboflow](https://www.g2.com/products/roboflow) — computer vision datasets and annotations. ### Data Labeling platforms that maintain annotation quality while handling high-volume image processing without slowdowns According to verified users, teams evaluating data labeling software often look for a balance between speed, consistency, and review controls. Recent G2 reviews highlight strengths such as built-in quality checks, version control, structured review steps, and collaboration features that help maintain labeling quality at scale. Reviewers also repeatedly call out limits to watch for, including lag with very large datasets, slower uploads or exports, and occasional responsiveness issues on heavy image workloads. In this category, buyers tend to compare how well platforms support organized dataset management, annotation review, and reliable throughput when image volume rises, rather than looking for raw processing speed alone. ### Which Data Labeling tools avoid performance issues and instability when processing large image batches Based on G2 reviews, these products are commonly mentioned for managing larger image workloads with structured workflows. - [SuperAnnotate](https://www.g2.com/products/superannotate) — large datasets with quality controls. - [Roboflow](https://www.g2.com/products/roboflow) — image annotation and dataset versioning. - [Taskmonk](https://www.g2.com/products/taskmonk) — scalable labeling with built-in QC. - [Encord](https://www.g2.com/products/encord) — video and data pipeline workflows. ### What are the most important features in data labeling software According to verified users, the most important features in data labeling software are annotation tools that match the data type, clear review and quality control workflows, dataset organization, and collaboration support. Recent G2 reviews also point to versioning, preprocessing or augmentation support, export flexibility, and AI-assisted labeling as recurring decision factors. For computer vision use cases, buyers often mention support for bounding boxes, polygons, segmentation, and video or point cloud workflows. Reviewers also care about how easily teams can move from labeling into model training or downstream pipelines. In practice, the best-fit platforms reduce manual effort while still helping teams maintain consistency, traceability, and clean handoffs. ### How do teams use Data Labeling for quality control According to verified users, teams use data labeling for quality control by building review steps directly into annotation workflows. Recent G2 reviews describe practices such as peer review, verify-before-submit checkpoints, role-based task assignment, change tracking, and version control to catch mistakes early and improve consistency across annotators. Buyers also look for tools that centralize labeling, feedback, and project management so fewer issues slip through handoffs. In image-heavy workflows, users value features that help compare annotations, review edge cases, and maintain standards across large batches. The common theme is that quality control works best when it is embedded in the labeling process rather than handled as a separate cleanup step later. ### [Amazon Sagemaker Ground Truth](https://www.g2.com/fr/products/amazon-sagemaker-ground-truth/reviews) Amazon SageMaker Ground Truth vous aide à créer rapidement des ensembles de données d'entraînement très précis pour l'apprentissage automatique. SageMaker Ground Truth offre un accès facile à des étiqueteurs humains publics et privés et leur fournit des flux de travail et des interfaces intégrés pour les tâches d'étiquetage courantes. **Average Rating:** 4.1/5.0 **Total Reviews:** 19 #### How Do G2 Users Rate Amazon Sagemaker Ground Truth? - **Qualité de l’étiqueteuse:** 10.0/10 (Category avg: 8.9/10) - **Détection d’objets:** 10.0/10 (Category avg: 8.9/10) - **Types de données:** 10.0/10 (Category avg: 8.8/10) - **Facilité d’utilisation:** 8.3/10 (Category avg: 8.8/10) #### Who Is the Company Behind Amazon Sagemaker Ground Truth? - **Vendeur:** [Amazon Web Services (AWS)](https://www.g2.com/fr/sellers/amazon-web-services-aws-3e93cc28-2e9b-4961-b258-c6ce0feec7dd) - **Année de fondation:** 2006 - **Emplacement du siège social:** Seattle, WA - **Twitter:** @awscloud 2,232,483 abonnés Twitter - **Page LinkedIn®:** [www.linkedin.com](https://www.g2.com/fr/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=072881eee28a2afe24f8d1bda9f20e3e146b9fb4b214f216411ce2ed6898b31e&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Famazon-web-services%2F&secure%5Burl_type%5D=linkedin_company_website) 147,094 employés sur LinkedIn® - **Propriété:** NASDAQ: AMZN #### Who Uses This Product? - **Top Industries:** Technologie de l'information et services - **Company Size:** 37% Small, 37% Large #### What Are Recent G2 Reviews of Amazon Sagemaker Ground Truth? **["Le meilleur service d'étiquetage de données entièrement géré jamais."](https://www.g2.com/fr/survey_responses/amazon-sagemaker-ground-truth-review-4971883)** **Rating:** 5.0/5.0 stars _— Vithushan S._ [Read full review](https://www.g2.com/fr/survey_responses/amazon-sagemaker-ground-truth-review-4971883) **["Idéal pour les amateurs d'Agile."](https://www.g2.com/fr/survey_responses/amazon-sagemaker-ground-truth-review-5146806)** **Rating:** 4.0/5.0 stars _— Utilisateur vérifié à Logiciels informatiques_ [Read full review](https://www.g2.com/fr/survey_responses/amazon-sagemaker-ground-truth-review-5146806) #### What Are G2 Users Discussing About Amazon Sagemaker Ground Truth? - [Which of these types of workforce are available in Amazon SageMaker ground truth?](https://www.g2.com/fr/discussions/which-of-these-types-of-workforce-are-available-in-amazon-sagemaker-ground-truth) - [How much does ground truth cost?](https://www.g2.com/fr/discussions/how-much-does-ground-truth-cost) - [Which type of data are included in Amazon SageMaker ground truth manifest file?](https://www.g2.com/fr/discussions/which-type-of-data-are-included-in-amazon-sagemaker-ground-truth-manifest-file) ### [V7 Darwin](https://www.g2.com/fr/products/v7-darwin/reviews) V7 Darwin est une plateforme d'IA spécialisée dans la création de données d'entraînement de haute qualité et la gestion des flux de travail d'annotation. Elle est conçue pour les équipes développant des modèles de vision par ordinateur sophistiqués et résolvant des défis complexes et spécifiques à un domaine avec l'IA. V7 Darwin offre une suite complète d'outils pour l'étiquetage de données, l'annotation vidéo et l'annotation d'imagerie médicale. - Créez des annotations d'images et de vidéos pixel-parfaites avec Auto-Annotate et SAM pour les masques sémantiques, la segmentation d'instances, les points clés et les polygones. - Développez l'IA médicale avec des outils pour l'annotation DICOM, NIfTI et WSI, avec une interface comprenant MPR, rendu 3D, réticules précis, fenêtrage et vues obliques. - Accélérez l'annotation vidéo jusqu'à 10 fois avec le suivi automatique assisté par IA pour les objets à travers les images. - Gérez de longues vidéos, des vues multi-caméras et des classes d'annotation imbriquées. - Concevez des flux de travail de révision multi-étapes avec logique conditionnelle, consensus et attribution de tâches pour votre pipeline d'étiquetage de données. - Organisez, filtrez et gérez de grands ensembles de données avec des vues et des tags personnalisés, permettant une collaboration en temps réel pour les annotateurs, les réviseurs et les ingénieurs ML. - Évoluez vos projets d'annotation avec des services professionnels d'étiquetage de données, y compris des annotateurs certifiés et des experts dans divers domaines (médical, vidéo, LLMs, scientifique). Vous pouvez intégrer V7 Darwin de manière transparente avec votre pile technologique existante et importer/exporter des annotations facilement. Obtenez un contrôle complet sur vos modèles, tâches et ensembles de données grâce à l'API ouverte, le SDK Darwin-py et l'interface en ligne de commande. **Average Rating:** 4.7/5.0 **Total Reviews:** 55 #### How Do G2 Users Rate V7 Darwin? - **Qualité de l’étiqueteuse:** 9.4/10 (Category avg: 8.9/10) - **Détection d’objets:** 9.4/10 (Category avg: 8.9/10) - **Types de données:** 9.2/10 (Category avg: 8.8/10) - **Facilité d’utilisation:** 9.5/10 (Category avg: 8.8/10) #### Who Is the Company Behind V7 Darwin? - **Vendeur:** [V7](https://www.g2.com/fr/sellers/v7) - **Année de fondation:** 2018 - **Emplacement du siège social:** London, England - **Twitter:** @v7labs 3,471 abonnés Twitter - **Page LinkedIn®:** [www.linkedin.com](https://www.g2.com/fr/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=de877e1b154c6b0c137e7416f2b5d21ab1d53e5f5fc06f8706ec44d415a22436&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fv7labs%2F&secure%5Burl_type%5D=linkedin_company_website) 106 employés sur LinkedIn® #### Who Uses This Product? - **Top Industries:** Technologie de l'information et services, Logiciels informatiques - **Company Size:** 55% Small, 35% Medium #### What Do G2 Reviewers Say About V7 Darwin? _AI-generated summary from verified user reviews_ ##### Pros - Les utilisateurs adorent la **facilité d'utilisation** de V7 Darwin, simplifiant l'annotation et la gestion de projet avec une interface intuitive. - Les utilisateurs apprécient l' **efficacité de l'annotation** de V7 Darwin, permettant un étiquetage facile des ensembles de données complexes et améliorant la productivité. - Les utilisateurs trouvent les **outils d'annotation automatique** de V7 Darwin extrêmement utiles pour un étiquetage de données facile et efficace. - Les utilisateurs apprécient l' **intégration innovante de LLM** dans V7 Darwin, améliorant la fonctionnalité des feuilles de calcul avec un support de contenu riche. - Les utilisateurs louent l' **efficacité** de V7 Darwin, rationalisant les processus RH et améliorant la productivité grâce à l'automatisation. ##### Cons - Les utilisateurs trouvent le **manque de fonctionnalités** frustrant, notamment en ce qui concerne la gestion des annotations et les options d'exportation pour les ensembles de données. - Les utilisateurs trouvent **les fonctionnalités manquantes** comme l'absence d'options de rétractation et les formats d'exportation limités frustrants dans V7 Darwin. - Les utilisateurs trouvent les **fonctionnalités limitées** de V7 Darwin frustrantes, désirant plus de flexibilité dans les annotations et la manipulation des ensembles de données. - Les utilisateurs sont frustrés par les **problèmes d'annotation** qui entravent leur capacité à retirer ou approuver les soumissions en douceur. - Les utilisateurs trouvent la **navigation difficile** de V7 Darwin frustrante, car elle entrave leur capacité à utiliser les fonctionnalités efficacement. #### What Are Recent G2 Reviews of V7 Darwin? **["SIRH complet pour la gestion du cycle de vie des employés de bout en bout"](https://www.g2.com/fr/survey_responses/v7-darwin-review-11727041)** **Rating:** 4.0/5.0 stars _— Shiv S._ [Read full review](https://www.g2.com/fr/survey_responses/v7-darwin-review-11727041) **["Annotation vidéo facile et étiquetage prédictif pour des ensembles de données massifs"](https://www.g2.com/fr/survey_responses/v7-darwin-review-12700843)** **Rating:** 4.5/5.0 stars _— Jed D._ [Read full review](https://www.g2.com/fr/survey_responses/v7-darwin-review-12700843) #### What Are G2 Users Discussing About V7 Darwin? - [À quoi sert V7 ?](https://www.g2.com/fr/discussions/what-is-v7-used-for) ### [Sama](https://www.g2.com/fr/products/sama/reviews) Sama est un leader mondialement reconnu dans les solutions d'annotation de données pour la vision par ordinateur d'entreprise et les modèles d'IA générative qui nécessitent la plus haute précision. En tant que pionnier de l'industrie avec 15 ans d'expérience, l'expertise et les solutions de Sama sont de confiance pour des entreprises de premier plan telles que GM, Ford, Continental, Google, et bien d'autres. Sama se spécialise dans les services d'annotation de données pour l'IA générative, et les images et vidéos 2D et 3D (y compris LiDAR et fusion de capteurs). Nous validons également des algorithmes d'apprentissage automatique complexes. En tant que leader de l'IA éthique et B-Corp certifié, nous avons été pionniers d'un modèle d'impact qui exploite le pouvoir des marchés pour le bien social. Nous avons amélioré de manière significative les résultats en matière d'emploi et de revenus pour ceux qui rencontrent les plus grands obstacles au travail formel (validé par une étude indépendante du MIT). Jusqu'à présent, nous avons aidé plus de 60 000 personnes à sortir de la pauvreté. **Average Rating:** 4.6/5.0 **Total Reviews:** 11 #### How Do G2 Users Rate Sama? - **Qualité de l’étiqueteuse:** 9.0/10 (Category avg: 8.9/10) - **Détection d’objets:** 9.6/10 (Category avg: 8.9/10) - **Types de données:** 9.6/10 (Category avg: 8.8/10) - **Facilité d’utilisation:** 9.2/10 (Category avg: 8.8/10) #### Who Is the Company Behind Sama? - **Vendeur:** [Sama](https://www.g2.com/fr/sellers/sama) - **Année de fondation:** 2008 - **Emplacement du siège social:** San Francisco, US - **Twitter:** @SamaAI 228,871 abonnés Twitter - **Page LinkedIn®:** [www.linkedin.com](https://www.g2.com/fr/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=05e2827df365f003c18aadac7b3a03b2ddcf89a9cc0237cf0288b460cca1fcfb&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F410136&secure%5Burl_type%5D=linkedin_company_website) 4,342 employés sur LinkedIn® #### Who Uses This Product? - **Company Size:** 55% Small, 36% Large #### What Do G2 Reviewers Say About Sama? _AI-generated summary from verified user reviews_ ##### Pros - Les utilisateurs apprécient les **fonctionnalités puissantes d'analytique et de préparation des données** de Sama, améliorant leurs perspectives et leur efficacité. - Les utilisateurs apprécient le **support client 24/7** de Sama, le trouvant utile en plus des tutoriels et des démonstrations pour une meilleure utilisation. - Les utilisateurs louent Sama pour ses **incroyables fonctionnalités de catalogage de données** , améliorant la préparation des données et fournissant des insights rapides. - Les utilisateurs apprécient les **incroyables fonctionnalités de prétraitement et d'enrichissement des données** de Sama, améliorant leur expérience analytique. - Les utilisateurs apprécient les **fonctionnalités étonnantes de prétraitement et d'enrichissement des données** de Sama, améliorant leurs capacités d'analyse. ##### Cons - Les utilisateurs trouvent le produit **complexe et difficile à utiliser** , nécessitant une formation approfondie ou du personnel qualifié pour fonctionner efficacement. - Les utilisateurs trouvent que la **configuration complexe** de Sama nécessite une formation approfondie ou du personnel qualifié pour fonctionner efficacement. - Les utilisateurs ont du mal avec le **manque de formation** requis pour Sama, le trouvant complexe et difficile à utiliser efficacement. - Les utilisateurs trouvent les **exigences de formation étendues** de Sama difficiles, ce qui rend son fonctionnement efficace difficile. #### What Are Recent G2 Reviews of Sama? **["Traduit par une meilleure performance du modèle"](https://www.g2.com/fr/survey_responses/sama-review-9669978)** **Rating:** 4.5/5.0 stars _— Mohammad A._ [Read full review](https://www.g2.com/fr/survey_responses/sama-review-9669978) **["Précision impressionnante sur leurs annotations de données"](https://www.g2.com/fr/survey_responses/sama-review-9935840)** **Rating:** 4.5/5.0 stars _— Nikita D._ [Read full review](https://www.g2.com/fr/survey_responses/sama-review-9935840) ### [Outlier AI](https://www.g2.com/fr/products/outlier-ai-outlier-ai/reviews) Outlier AI est une plateforme qui connecte l'expertise humaine avec l'intelligence artificielle pour améliorer la précision, la rapidité et la fiabilité des modèles d'IA. En engageant un réseau mondial de plus de 100 000 experts dans plus de 50 pays, Outlier AI a facilité le développement de systèmes d'IA plus informés et percutants, distribuant plus de 500 millions de dollars à ses contributeurs. Caractéristiques clés et fonctionnalités : - Formation d'IA dirigée par des experts : Outlier AI exploite les compétences spécialisées de sa communauté mondiale d'experts pour former et affiner les modèles d'IA, garantissant une annotation de données de haute qualité et un développement de modèles. - Opportunités de travail à distance flexibles : La plateforme offre aux individus des opportunités d'emploi significatives et accessibles, permettant aux experts de contribuer à distance et selon leurs propres horaires. - Intégration avec Scale AI : Propulsé par Scale AI, Outlier AI combine une infrastructure de données de premier ordre avec des capacités avancées de détection d'anomalies, améliorant la scalabilité et la précision des solutions d'IA. Valeur principale et problème résolu : Outlier AI répond au défi de développer des modèles d'IA fiables et efficaces en intégrant l'expertise humaine dans le processus de formation de l'IA. Cette approche améliore non seulement la qualité des résultats de l'IA, mais offre également des opportunités d'emploi flexibles à une main-d'œuvre mondiale diversifiée. En comblant le fossé entre l'intelligence humaine et l'intelligence artificielle, Outlier AI garantit que les systèmes d'IA sont plus précis, efficaces et alignés sur les applications du monde réel. **Average Rating:** 4.0/5.0 **Total Reviews:** 14 #### How Do G2 Users Rate Outlier AI? - **Qualité de l’étiqueteuse:** 9.6/10 (Category avg: 8.9/10) - **Détection d’objets:** 9.2/10 (Category avg: 8.9/10) - **Types de données:** 10.0/10 (Category avg: 8.8/10) - **Facilité d’utilisation:** 8.1/10 (Category avg: 8.8/10) #### Who Is the Company Behind Outlier AI? - **Vendeur:** [Outlier AI](https://www.g2.com/fr/sellers/outlier-ai) - **Année de fondation:** 2023 - **Emplacement du siège social:** San Francisco, US - **Page LinkedIn®:** [www.linkedin.com](https://www.g2.com/fr/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=cd7fc401ed04daac65acf0d9fb4d1889c1db791ed38161618afccb1b10a73dfa&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Ftry-outlier&secure%5Burl_type%5D=linkedin_company_website) 28,516 employés sur LinkedIn® #### Who Uses This Product? - **Top Industries:** Technologie de l'information et services - **Company Size:** 100% Small #### What Do G2 Reviewers Say About Outlier AI? _AI-generated summary from verified user reviews_ ##### Pros - Les utilisateurs louent l'équipe **d'assistance clientèle utile** d'Outlier AI, améliorant ainsi leur expérience et leur satisfaction globales. - Les utilisateurs apprécient la **haute précision des données** d'Outlier AI, appréciant son engagement envers des réponses bien réfléchies. - Les utilisateurs apprécient la **flexibilité** d'Outlier AI, permettant le travail à distance sur des projets et le freelancing basé sur les compétences. - Les utilisateurs apprécient le **système de paiement transparent et rapide** d'Outlier AI, ce qui améliore leur expérience globale et leur satisfaction. - Les utilisateurs soulignent la **vitesse de réponse rapide** d'Outlier AI, améliorant ainsi leur expérience globale avec la plateforme. ##### Cons - Les utilisateurs notent des **interruptions de travail** dues à une disponibilité de projet incohérente et à des évaluations difficiles, impactant leur expérience globale. - Les utilisateurs signalent une **performance boguée** avec Outlier AI, entraînant des évaluations de projet incohérentes et des suppressions inattendues de tâches. - Les utilisateurs sont frustrés par la **faible rémunération** et la disponibilité incohérente des projets, ce qui affecte leur expérience globale avec Outlier AI. - Les utilisateurs notent que les **problèmes de performance** entravent une expérience plus rapide et plus fluide avec Outlier AI, affectant l'utilisabilité. - Les utilisateurs sont confrontés à un **mauvais service client** avec des changements de travail non annoncés et une communication inadéquate de la part de la direction, ce qui provoque de la frustration. #### What Are Recent G2 Reviews of Outlier AI? **["Flux de travail flexible et fluide pour des projets d'IA significatifs"](https://www.g2.com/fr/survey_responses/outlier-ai-review-12904617)** **Rating:** 4.0/5.0 stars _— Utilisateur vérifié à Technologie de l'information et services_ [Read full review](https://www.g2.com/fr/survey_responses/outlier-ai-review-12904617) **["Le rêve d'un développeur à l'intersection de l'IA"](https://www.g2.com/fr/survey_responses/outlier-ai-review-12834046)** **Rating:** 4.5/5.0 stars _— Utilisateur vérifié à Logiciels informatiques_ [Read full review](https://www.g2.com/fr/survey_responses/outlier-ai-review-12834046) ### [Keymakr](https://www.g2.com/products/keymakr/reviews) We are a data labeling company that focuses on providing high quality annotation services and excellent customer support. We are the best choice for: Image Annotation Video Annotation Data validation Document Annotation Data Creation Data Collection Our company creates best-in-class computer vision training data. We offer an in-house team paired with advanced proprietary annotation tools. Scalable and secure one-stop shop for your AI **Average Rating:** 4.8/5.0 **Total Reviews:** 45 #### How Do G2 Users Rate Keymakr? - **Labeler Quality:** 9.4/10 (Category avg: 8.9/10) - **Object Detection:** 9.7/10 (Category avg: 8.9/10) - **Data Types:** 9.7/10 (Category avg: 8.8/10) - **Ease of Use:** 9.2/10 (Category avg: 8.8/10) #### Who Is the Company Behind Keymakr? - **Seller:** [Keymakr](https://www.g2.com/sellers/keymakr) - **Year Founded:** 2015 - **HQ Location:** New York, NY - **Twitter:** @keymakr\_com 354 Twitter followers - **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=5fb808098ef63b965158417815d66f2ac2810beadbf8235c0f26f8875a0b72ae&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fkeymakr%2F&secure%5Burl_type%5D=linkedin_company_website) 69 employees on LinkedIn® #### Who Uses This Product? - **Top Industries:** Computer Software - **Company Size:** 52% Small, 22% Medium #### What Do G2 Reviewers Say About Keymakr? _AI-generated summary from verified user reviews_ ##### Pros - Users commend Keymakr for their **exceptional customer support** , highlighting fast responses and a proactive service approach. - Users appreciate Keymakr's **efficiency** in integrating with workflows, streamlining processes, and enhancing productivity effortlessly. - Users find the **initial setup incredibly easy** , enjoying a smooth start with Keymakr's efficient implementation process. - Users value the **efficient annotation process** of Keymakr, enhancing workflow and ensuring quality results quickly. - Users value the **excellent data management** of Keymakr, appreciating the team's commitment to quality and client satisfaction. ##### Cons - Users find the **difficult setup** challenging, with convoluted navigation and connection issues during initial use. - Users face **recurring annotation mistakes** , though improvements in data quality and processes have enhanced results over time. - Users face challenges with **complex UI navigation** and initial setup, particularly with S3 connectivity. - Users find the **limited customization** of Keymakr frustrating, particularly the inability to change passwords independently. #### What Are Recent G2 Reviews of Keymakr? **["Accurate Annotation, Valuable Results"](https://www.g2.com/survey_responses/keymakr-review-11103169)** **Rating:** 4.5/5.0 stars _— Rinat L._ [Read full review](https://www.g2.com/survey_responses/keymakr-review-11103169) **["A true Partner in Image Recognition"](https://www.g2.com/survey_responses/keymakr-review-11968704)** **Rating:** 5.0/5.0 stars _— Yacine B._ [Read full review](https://www.g2.com/survey_responses/keymakr-review-11968704) ### [Taskmonk](https://www.g2.com/fr/products/taskmonk/reviews) Taskmonk est la plateforme de données d'entraînement IA pour les entreprises qui ont besoin d'annotations à l'échelle de production, et non à l'échelle pilote. C'est l'une des rares plateformes qui gère six modalités de données : texte, image, audio, vidéo, LiDAR et DICOM dans un seul environnement, de sorte que les équipes ne doivent pas assembler des outils séparés une fois qu'un projet dépasse la vision par ordinateur standard. Conçu pour les équipes d'annotation, les chefs de projet et les responsables IA, Taskmonk associe un créateur de flux de travail sans code à un étiquetage assisté par IA et un contrôle qualité qui tient la route à grande échelle : • Flux de travail sans code/à faible code qui s'adaptent par projet sans temps d'ingénierie • Étiquetage assisté par IA et pré-étiquetage assisté par modèle qui réduisent les heures d'annotation et augmentent le débit • Contrôle qualité multi-étapes comme les ensembles d'or, le consensus, l'arbitrage, plus le routage des tâches basé sur l'affinité par performance de l'annotateur, pas seulement par rôle • Conformité SOC 2 Type II et ISO 27001, avec HIPAA et résidence des données disponibles pour les industries réglementées Taskmonk a traité plus de 480 millions de tâches d'annotation et plus de 6 millions d'heures d'étiquetage pour plus de 10 clients du Fortune 500, y compris Flipkart, Myntra et LG, économisant plus de 10 millions de dollars à ce jour. Les équipes peuvent également intégrer une main-d'œuvre d'annotation vérifiée et soutenue par un SLA sur la même plateforme, de sorte qu'il n'y a qu'un seul contrat et un seul chemin d'escalade au lieu de rechercher séparément des talents pour l'étiquetage. **Average Rating:** 4.6/5.0 **Total Reviews:** 17 #### How Do G2 Users Rate Taskmonk? - **Qualité de l’étiqueteuse:** 9.3/10 (Category avg: 8.9/10) - **Détection d’objets:** 9.2/10 (Category avg: 8.9/10) - **Types de données:** 9.7/10 (Category avg: 8.8/10) - **Facilité d’utilisation:** 9.2/10 (Category avg: 8.8/10) #### Who Is the Company Behind Taskmonk? - **Vendeur:** [Taskmonk](https://www.g2.com/fr/sellers/taskmonk) - **Année de fondation:** 2018 - **Emplacement du siège social:** Bengaluru, Karnataka, India - **Twitter:** @TaskmonkAI 16 abonnés Twitter - **Page LinkedIn®:** [www.linkedin.com](https://www.g2.com/fr/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=1d6fc148b17fe971252bbfff900d04da0cb064fa240b87046de8c9b9bcd442f6&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Ftaskmonk%2F&secure%5Burl_type%5D=linkedin_company_website) 28 employés sur LinkedIn® #### Who Uses This Product? - **Top Industries:** Technologie de l'information et services - **Company Size:** 72% Small, 22% Large #### What Do G2 Reviewers Say About Taskmonk? _AI-generated summary from verified user reviews_ ##### Pros - Les utilisateurs trouvent que Taskmonk est **incroyablement facile à utiliser** , avec une configuration rapide et une interface intuitive pour tous les niveaux. - Les utilisateurs félicitent Taskmonk pour son **support client exceptionnel** , notant leur réactivité et leur dévouement à aider les clients à réussir. - Les utilisateurs soulignent l' **efficacité** de Taskmonk, permettant des configurations rapides et une gestion de projet fluide pour des tâches à volume élevé. - Les utilisateurs apprécient l' **interface intuitive et les fonctionnalités robustes** de Taskmonk, rendant l'étiquetage des données efficace et convivial. - Les utilisateurs louent la **configuration rapide et facile** de Taskmonk, permettant une intégration rapide et fluide dans les flux de travail. ##### Cons - Les utilisateurs trouvent que le **manque de fonctionnalités** limite les rapports avancés et la flexibilité, affectant l'utilisabilité et l'expérience. - Les utilisateurs trouvent la **courbe d'apprentissage difficile** difficile, surtout pour les débutants qui naviguent dans l'interface utilisateur complexe. - Les utilisateurs trouvent que **la complexité de l'interface utilisateur** est accablante, en particulier avec le nombre d'options présentées en même temps. - Les utilisateurs rencontrent occasionnellement des **difficultés techniques** , mais l'équipe Taskmonk résout rapidement les problèmes et fournit des solutions. - Les utilisateurs trouvent les **problèmes de téléchargement** frustrants, en particulier avec les restrictions du cloud et les retards pendant les heures de pointe qui impactent l'efficacité. #### What Are Recent G2 Reviews of Taskmonk? **["Produit impressionnant !"](https://www.g2.com/fr/survey_responses/taskmonk-review-11562637)** **Rating:** 4.0/5.0 stars _— Aditya N._ [Read full review](https://www.g2.com/fr/survey_responses/taskmonk-review-11562637) **["Un outil convivial pour les débutants, rapide et hautement personnalisable pour tout type de processus."](https://www.g2.com/fr/survey_responses/taskmonk-review-11752427)** **Rating:** 5.0/5.0 stars _— JAYAPRAKASH K._ [Read full review](https://www.g2.com/fr/survey_responses/taskmonk-review-11752427) ### [Appen](https://www.g2.com/fr/products/appen/reviews) Appen collecte et étiquette des images, du texte, de la parole, de l'audio, de la vidéo et d'autres données pour créer des données d'entraînement utilisées pour construire et améliorer continuellement les systèmes d'intelligence artificielle les plus innovants au monde. Nous offrons une plateforme d'annotation de données de pointe et sous licence pour annoter des cas d'utilisation de données d'entraînement en vision par ordinateur et en traitement du langage naturel. Notre plateforme améliore la précision et l'efficacité grâce à nos fonctionnalités d'étiquetage intelligent et de pré-étiquetage qui utilisent l'apprentissage automatique pour faciliter les annotations humaines. Vous choisissez le niveau de service et de sécurité que vous souhaitez pour la collecte et l'annotation des données, allant du service géré haut de gamme au libre-service flexible. Notre expertise inclut une foule mondiale de plus de 1 million de contractants qualifiés qui parlent plus de 235 langues et dialectes, dans plus de 70 000 lieux et 170 pays, et la plateforme d'annotation de données assistée par IA la plus avancée de l'industrie. Nos données d'entraînement fiables donnent aux leaders de la technologie, de l'automobile, des services financiers, de la vente au détail, de la santé et des gouvernements la confiance nécessaire pour déployer des produits d'IA de classe mondiale. Fondée en 1996, Appen a des clients et des bureaux dans le monde entier. **Average Rating:** 4.2/5.0 **Total Reviews:** 33 #### How Do G2 Users Rate Appen? - **Qualité de l’étiqueteuse:** 8.5/10 (Category avg: 8.9/10) - **Détection d’objets:** 8.7/10 (Category avg: 8.9/10) - **Types de données:** 8.8/10 (Category avg: 8.8/10) - **Facilité d’utilisation:** 8.2/10 (Category avg: 8.8/10) #### Who Is the Company Behind Appen? - **Vendeur:** [Appen](https://www.g2.com/fr/sellers/appen) - **Année de fondation:** 1996 - **Emplacement du siège social:** Kirkland, Washington, United States - **Page LinkedIn®:** [www.linkedin.com](https://www.g2.com/fr/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=b0931c0a7f2c53197a2b59e80de4a7fdfa27c61180c1a3b6c615dca728909c73&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fappen&secure%5Burl_type%5D=linkedin_company_website) 20,647 employés sur LinkedIn® - **Propriété:** ASX:APX - **Revenu total (en MM USD):** $244,900 #### Who Uses This Product? - **Top Industries:** Technologie de l'information et services - **Company Size:** 54% Small, 26% Large #### What Do G2 Reviewers Say About Appen? _AI-generated summary from verified user reviews_ ##### Pros - Les utilisateurs apprécient la **flexibilité** d'Appen, leur permettant de s'engager dans des projets divers et intéressants depuis leurs téléphones portables. - Les utilisateurs apprécient la **facilité d'utilisation** d'Appen, permettant l'accomplissement des tâches sans effort via leurs téléphones portables. - Les utilisateurs apprécient la **flexibilité** d'Appen, permettant des projets engageants et diversifiés qui améliorent leur expérience de travail. ##### Cons - Les utilisateurs subissent des **interruptions de travail** fréquentes, rencontrant des défis tels que la disponibilité incohérente et des processus de qualification longs. - Les utilisateurs éprouvent une **faible compensation** en raison de la disponibilité de travail incohérente et des paiements mensuels, ce qui affecte la stabilité des revenus. - Les utilisateurs trouvent la **navigation déroutante** et subissent des déconnexions fréquentes, ce qui entraîne de la frustration lors de l'utilisation d'Appen. - Les utilisateurs rencontrent souvent des **problèmes de connectivité** , éprouvant de la confusion lors de la navigation et des déconnexions fréquentes de l'application. - Les utilisateurs trouvent la **navigation déroutante** , ce qui entraîne souvent des déconnexions inattendues qui perturbent leur expérience avec Appen. #### What Are Recent G2 Reviews of Appen? **["Plateforme de crowdsourcing robuste pour les tâches d'IA et de langage"](https://www.g2.com/fr/survey_responses/appen-review-12769449)** **Rating:** 4.0/5.0 stars _— Sina A._ [Read full review](https://www.g2.com/fr/survey_responses/appen-review-12769449) **["Idéal pour les freelances, simplicité avec une marge d'amélioration pour le support"](https://www.g2.com/fr/survey_responses/appen-review-12550258)** **Rating:** 5.0/5.0 stars _— Ashish S._ [Read full review](https://www.g2.com/fr/survey_responses/appen-review-12550258) ### [Kili](https://www.g2.com/products/kili/reviews) Kili Technology is a collaborative AI data platform designed to meet the rigorous needs of building large-scale production-ready AI data securely. Founded in Paris in 2018, Kili Technology caters to a diverse range of industries, including healthcare, financial services, manufacturing, defense, and technology. The platform is engineered to support teams of varying sizes, accommodating anywhere from 1 to over 500 concurrent users, and processes millions of assets annually. The core functionality of Kili Technology lies in its ability to facilitate collaboration among cross-functional teams. Unlike traditional labeling tools that primarily serve machine learning engineers, Kili connects data science teams with business stakeholders and subject matter experts. This integration enhances the AI development lifecycle by streamlining processes from annotation and labeling to validation and model feedback. As a result, users can ensure that the data used for training AI models is not only accurate but also relevant to the specific business context. Kili Technology is particularly beneficial for organizations looking to harness the power of AI while maintaining a high level of data quality. The platform supports various data modalities, allowing teams to work with text, images, audio, and video data seamlessly. This versatility makes it suitable for a wide range of applications, from developing natural language processing models to image recognition systems. By fostering collaboration among different roles within an organization, Kili enhances the overall efficiency of the AI development process. Key features of Kili Technology include an intuitive user interface that simplifies the labeling process, robust tools for data validation, and comprehensive feedback mechanisms that enable continuous improvement of AI models. Additionally, the platform offers advanced analytics capabilities, allowing teams to track progress and identify areas for enhancement. These features collectively empower organizations to build high-quality training datasets that meet the demands of complex AI applications. Kili Technology stands out in the competitive landscape of AI data platforms by prioritizing collaboration and usability. By bridging the gap between technical and non-technical stakeholders, it ensures that the development of AI solutions is a cohesive effort. This approach not only accelerates the time to market for AI initiatives but also enhances the overall quality of the training data, ultimately leading to more effective AI models. **Average Rating:** 4.7/5.0 **Total Reviews:** 52 #### How Do G2 Users Rate Kili? - **Labeler Quality:** 9.2/10 (Category avg: 8.9/10) - **Object Detection:** 9.2/10 (Category avg: 8.9/10) - **Data Types:** 9.2/10 (Category avg: 8.8/10) - **Ease of Use:** 8.9/10 (Category avg: 8.8/10) #### Who Is the Company Behind Kili? - **Seller:** [Kili Technology](https://www.g2.com/sellers/kili-technology) - **Company Website:** kili-technology.com - **Year Founded:** 2018 - **HQ Location:** Paris, FR - **Twitter:** @Kili\_Technology 438 Twitter followers - **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=528a4a4dc014455d66d25ef4421bc2e8c6ca341820ef9e1a434c4f0e6a6e0e80&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F33266852&secure%5Burl_type%5D=linkedin_company_website) 48 employees on LinkedIn® #### Who Uses This Product? - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 38% Medium, 34% Small #### What Do G2 Reviewers Say About Kili? _AI-generated summary from verified user reviews_ ##### Pros - Users love the **ease of use** of Kili's annotation platform and appreciate its comprehensive metrics for projects. - Users value the **ease of use** and precise metrics for comprehensive project visualization on Kili's annotation platform. - Users love the **ease of use** offered by Kili, enhancing their annotation experience significantly. - Users love the **variety of models** available on Kili, enhancing their annotation projects with tailored solutions. ##### Cons - Users feel that Kili lacks **adequate content updates** , limiting the platform's overall utility and engagement. - Users feel Kili lacks **adequate content updates** , limiting their overall experience and engagement with the platform. #### What Are Recent G2 Reviews of Kili? **["Intuitive UX and Quick Installation"](https://www.g2.com/survey_responses/kili-review-12342244)** **Rating:** 5.0/5.0 stars _— Hery R._ [Read full review](https://www.g2.com/survey_responses/kili-review-12342244) **["Ease of Use and Exceptional Efficiency"](https://www.g2.com/survey_responses/kili-review-12354373)** **Rating:** 5.0/5.0 stars _— Lantosoa V._ [Read full review](https://www.g2.com/survey_responses/kili-review-12354373) ### [Datature](https://www.g2.com/products/datature/reviews) Datature is an AI Vision platform that simplifies computer vision development by unifying data labeling, model training, and deployment into a single workflow. By eliminating the need for fragmented tools and complex infrastructure, teams can focus on solving real-world problems. **Average Rating:** 4.9/5.0 **Total Reviews:** 39 #### How Do G2 Users Rate Datature? - **Labeler Quality:** 9.5/10 (Category avg: 8.9/10) - **Object Detection:** 9.9/10 (Category avg: 8.9/10) - **Data Types:** 8.9/10 (Category avg: 8.8/10) - **Ease of Use:** 9.5/10 (Category avg: 8.8/10) #### Who Is the Company Behind Datature? - **Seller:** [Datature](https://www.g2.com/sellers/datature) - **Year Founded:** 2020 - **HQ Location:** San Francisco, US - **Twitter:** @DatatureAI 168 Twitter followers - **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=63ff7ae5a5cc37c5d990c326e31c4bcfe336374f4a4d8bc4267e834b2e8dc247&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fdatature%2F&secure%5Burl_type%5D=linkedin_company_website) 23 employees on LinkedIn® #### Who Uses This Product? - **Top Industries:** Computer Software, Research - **Company Size:** 64% Small, 28% Large #### What Do G2 Reviewers Say About Datature? _AI-generated summary from verified user reviews_ ##### Pros - Users praise the **efficiency** of Datature, enabling quick data labeling and streamlined model training for faster project progress. - Users value the **annotation efficiency** of Datature, streamlining their machine learning projects with user-friendly tools. - Users find Datature to be **extremely user-friendly** , simplifying data labeling and model training for all skill levels. - Users value the **extensive model types** and options in Datature, praising its support and user-friendly design. - Users appreciate the **efficient AI capabilities** of Datature, enhancing data labeling and model training processes significantly. ##### Cons - Users find **limited customization options** in Datature may hinder advanced users seeking more control over their setups. - Users experience **annotation tool clarity issues** , yet prompt support in Slack helps resolve problems quickly. - Users experience a **difficult learning curve** when setting up labeling jobs, despite finding model building easy. - Users face a **difficult setup** for labeling jobs, which may hinder their initial experience with Datature. - Users note the **high cost** of Datature, which may be a barrier for personal users despite its quality. #### What Are Recent G2 Reviews of Datature? **["Impressive range of CV models makes custom Vision AI projects easy"](https://www.g2.com/survey_responses/datature-review-12102117)** **Rating:** 4.5/5.0 stars _— Phil B._ [Read full review](https://www.g2.com/survey_responses/datature-review-12102117) **["Extremely User-Friendly Platform Backed by a Kind and Committed Team"](https://www.g2.com/survey_responses/datature-review-13081746)** **Rating:** 5.0/5.0 stars _— Sam R._ [Read full review](https://www.g2.com/survey_responses/datature-review-13081746) ### [CVAT.ai](https://www.g2.com/fr/products/cvat-ai/reviews) Aperçu de l'entreprise : CVAT.ai est un fournisseur mondial d'outils et de services d'annotation de données, connu pour avoir développé l'un des outils d'annotation open-source les plus populaires, CVAT. En plus de la plateforme open-source, nous offrons des services professionnels de labellisation de données, une version Entreprise de CVAT, ainsi que des services de conseil et de personnalisation pour répondre aux besoins spécifiques des clients. Notre équipe soutient les entreprises et les chercheurs en IA du monde entier dans la gestion efficace de l'annotation de données pour les projets de vision par ordinateur. Caractéristiques clés : - Outil Open-Source Populaire : CVAT est approuvé par des milliers de développeurs et d'organisations à l'échelle mondiale. - Services de Labellisation de Données : Nous fournissons des services experts de labellisation de données pour gérer les projets de bout en bout. - Version Entreprise de CVAT : La version Entreprise offre des fonctionnalités avancées, un support et une évolutivité pour les grandes organisations. - Conseil et Personnalisation : Nous offrons des services de conseil et pouvons personnaliser CVAT pour correspondre aux besoins de votre projet. En savoir plus sur notre approche du conseil et des demandes de fonctionnalités ici. - Automatisation Assistée par l'IA : Notre plateforme utilise l'IA pour améliorer l'efficacité et la précision de la labellisation. - Collaboration en Équipe : Les équipes peuvent collaborer sans heurts sur des projets à grande échelle. - Personnalisable et Évolutif : CVAT peut être adapté à la taille et aux besoins de votre projet. - Sécurisé : Nous respectons les normes mondiales de confidentialité et de sécurité des données. Ce que nous résolvons : CVAT.ai aide les utilisateurs à réduire les efforts manuels en rendant l'annotation de données plus rapide, plus précise et facile à gérer. Grâce à notre plateforme open-source, nos services professionnels de labellisation, notre conseil et la version Entreprise, nous offrons une solution flexible et complète pour tout projet de vision par ordinateur. **Average Rating:** 4.5/5.0 **Total Reviews:** 31 #### How Do G2 Users Rate CVAT.ai? - **Qualité de l’étiqueteuse:** 9.5/10 (Category avg: 8.9/10) - **Détection d’objets:** 9.2/10 (Category avg: 8.9/10) - **Types de données:** 8.6/10 (Category avg: 8.8/10) - **Facilité d’utilisation:** 8.8/10 (Category avg: 8.8/10) #### Who Is the Company Behind CVAT.ai? - **Vendeur:** [CVAT.ai](https://www.g2.com/fr/sellers/cvat-ai) - **Site Web de l'entreprise:** www.cvat.ai - **Année de fondation:** 2022 - **Emplacement du siège social:** Palo Alto, US - **Page LinkedIn®:** [www.linkedin.com](https://www.g2.com/fr/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=3fcff5bc744a0faed9f68d2f3d4c21c790b4a51f28c113fb6952ea0700f32084&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fcvat-ai%2F&secure%5Burl_type%5D=linkedin_company_website) 114 employés sur LinkedIn® #### Who Uses This Product? - **Company Size:** 71% Small, 19% Medium #### What Do G2 Reviewers Say About CVAT.ai? _AI-generated summary from verified user reviews_ ##### Pros - Les utilisateurs louent le **support client exceptionnel** de CVAT.ai, améliorant leur expérience grâce à une assistance et des conseils précieux. - Les utilisateurs louent la **vitesse d'annotation incroyable** de CVAT.ai, garantissant des résultats efficaces et de haute qualité pour des tâches complexes. - Les utilisateurs apprécient les **options de personnalisation** de CVAT.ai, permettant des projets d'annotation sur mesure et des configurations polyvalentes. - Les utilisateurs louent la **gestion efficace des données** de CVAT.ai, qui améliore le flux de travail et la collaboration dans les projets d'imagerie médicale. - Les utilisateurs apprécient les **options d'annotation polyvalentes** de CVAT.ai, permettant le support de divers algorithmes de vision par ordinateur. ##### Cons - Les utilisateurs trouvent la plateforme **difficile à apprendre** , surtout les débutants, en raison de sa complexité et de ses fonctionnalités chronophages. - Les utilisateurs trouvent que **la complexité de l'interface** peut être accablante, surtout pour les débutants qui naviguent dans les fonctionnalités. - Les utilisateurs remarquent des **incohérences d'étiquetage** dans les cas complexes, bien que l'équipe réponde rapidement aux retours pour améliorer la précision. - Les utilisateurs éprouvent un **manque de fonctionnalités** en raison de l'absence de support pour les grandes images 16 bits dans CVAT.ai. - Les utilisateurs rencontrent une **performance lente** avec CVAT.ai lors du traitement de fichiers vidéo volumineux et de nombreuses images. #### What Are Recent G2 Reviews of CVAT.ai? **["L'annotation assistée par modèle avec CVAT a transformé des semaines de travail en jours."](https://www.g2.com/fr/survey_responses/cvat-ai-review-12981074)** **Rating:** 4.0/5.0 stars _— Fabian v._ [Read full review](https://www.g2.com/fr/survey_responses/cvat-ai-review-12981074) **["Annotation d'image puissante avec des outils de pré-étiquetage et de révision"](https://www.g2.com/fr/survey_responses/cvat-ai-review-12936375)** **Rating:** 4.5/5.0 stars _— Katarzyna K._ [Read full review](https://www.g2.com/fr/survey_responses/cvat-ai-review-12936375) ### [Dataloop](https://www.g2.com/fr/products/dataloop-dataloop/reviews) Dataloop est une plateforme de développement d'IA de pointe qui transforme la manière dont les organisations construisent des applications d'IA. Notre plateforme est méticuleusement conçue pour répondre aux besoins des développeurs au cœur du processus de développement de l'IA, rendant le travail avec les données et les modèles d'IA plus simple et plus intuitif. Notre solution complète couvre l'ensemble du cycle de vie du développement de l'IA, offrant des outils et des fonctionnalités qui simplifient la gestion des données, l'annotation, la sélection de modèles et le déploiement. La plateforme de Dataloop est construite avec un accent sur la collaboration, permettant aux développeurs, scientifiques des données et ingénieurs de travailler ensemble de manière transparente, brisant les silos traditionnels et favorisant l'innovation. Les caractéristiques clés incluent une interface intuitive de glisser-déposer pour construire des pipelines de données, une vaste bibliothèque d'éléments et de modèles d'IA préconstruits, et des capacités robustes de curation et d'annotation de données. Ces fonctionnalités sont conçues pour permettre aux développeurs de prototyper, itérer et déployer rapidement des solutions d'IA, en suivant le rythme des demandes en constante évolution du marché. Dataloop s'engage à faire progresser le développement de l'IA en fournissant une plateforme centrée sur le développeur qui aborde les complexités et les défis de la gestion de l'IA et des données. Notre vision est de démocratiser le développement de l'IA, permettant à chaque organisation de tirer parti de la puissance de l'IA et de faire avancer leurs solutions innovantes. **Average Rating:** 4.4/5.0 **Total Reviews:** 87 #### How Do G2 Users Rate Dataloop? - **Qualité de l’étiqueteuse:** 8.8/10 (Category avg: 8.9/10) - **Détection d’objets:** 9.2/10 (Category avg: 8.9/10) - **Types de données:** 9.2/10 (Category avg: 8.8/10) - **Facilité d’utilisation:** 8.8/10 (Category avg: 8.8/10) #### Who Is the Company Behind Dataloop? - **Vendeur:** [Dataloop](https://www.g2.com/fr/sellers/dataloop) - **Année de fondation:** 2017 - **Emplacement du siège social:** Herzliya, IL - **Page LinkedIn®:** [www.linkedin.com](https://www.g2.com/fr/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=da9efd7c177fd7b05cc561f5bccd297b04f67ce9561db7a8c8492155b1c98891&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fdataloop&secure%5Burl_type%5D=linkedin_company_website) 52 employés sur LinkedIn® #### Who Uses This Product? - **Top Industries:** Logiciels informatiques, Technologie de l'information et services - **Company Size:** 39% Medium, 33% Small #### What Do G2 Reviewers Say About Dataloop? _AI-generated summary from verified user reviews_ ##### Pros - Les utilisateurs apprécient la **facilité d'utilisation** de Dataloop, trouvant l'interface intuitive et l'annotation simple et efficace. - Les utilisateurs apprécient l' **efficacité de l'annotation** de Dataloop, profitant d'une interface simple qui améliore l'expérience globale. - Les utilisateurs apprécient les **capacités d'annotation faciles** de Dataloop, bénéficiant de son interface simple et intuitive. - Les utilisateurs adorent l' **interface simple et facile à naviguer** de Dataloop, ce qui améliore leur expérience globale avec l'outil. - Les utilisateurs apprécient les **intégrations faciles** de Dataloop, améliorant leurs flux de travail existants sans effort. ##### Cons - Les utilisateurs trouvent les **changements de l'interface utilisateur compliqués** , rendant l'expérience globale avec Dataloop moins intuitive. - Les utilisateurs trouvent la **syntaxe déroutante** de Dataloop frustrante, en particulier après les récents changements d'interface utilisateur qui ont affecté l'utilisabilité. - Les utilisateurs trouvent la **navigation difficile** en raison des changements d'interface utilisateur déroutante, ce qui impacte leur expérience globale. - Les utilisateurs estiment que le **manque de communication** de la part de la communauté nuit à leur expérience globale avec Dataloop. - Les utilisateurs estiment que l'absence d'une **section de démonstration pour les nouveaux utilisateurs** entrave leur expérience d'intégration avec Dataloop. #### What Are Recent G2 Reviews of Dataloop? **["J'ai eu une expérience fluide et pratique chaque jour où je travaille sur Dataloop."](https://www.g2.com/fr/survey_responses/dataloop-review-9624539)** **Rating:** 5.0/5.0 stars _— Mzamil J._ [Read full review](https://www.g2.com/fr/survey_responses/dataloop-review-9624539) **["Un voyage dans le flux de travail des données avec Dataloop."](https://www.g2.com/fr/survey_responses/dataloop-review-9633025)** **Rating:** 4.0/5.0 stars _— Dennis R._ [Read full review](https://www.g2.com/fr/survey_responses/dataloop-review-9633025) #### What Are G2 Users Discussing About Dataloop? - [Qu'est-ce que les annotations de données ?](https://www.g2.com/fr/discussions/dataloop-what-are-data-annotations) - 1 comment - [Quelles sont les annotations de données ?](https://www.g2.com/fr/discussions/what-are-data-annotations) - 1 comment - [Dataloop est-il gratuit ?](https://www.g2.com/fr/discussions/dataloop-is-dataloop-free) - 1 comment - [Dataloop est-il gratuit ?](https://www.g2.com/fr/discussions/is-dataloop-free) - 1 comment - [What are the industries that Dataloop supports?](https://www.g2.com/fr/discussions/dataloop-what-are-the-industries-that-dataloop-supports) - 1 comment - ‹ Prev‹ Prev - 1 - [2](/categories/data-labeling?order=g2_score&page=2#product-list) - [3](/categories/data-labeling?order=g2_score&page=3#product-list) - [4](/categories/data-labeling?order=g2_score&page=4#product-list) - [5](/categories/data-labeling?order=g2_score&page=5#product-list) - … - [7](/categories/data-labeling?order=g2_score&page=7#product-list) - [8](/categories/data-labeling?order=g2_score&page=8#product-list) - [Next ›Next ›](/categories/data-labeling?order=g2_score&page=2#product-list) Spotlight Categories [Electronic Data Interchange (EDI) Software](https://www.g2.com/categories/electronic-data-interchange-edi) [Accounting Software](https://www.g2.com/categories/accounting) [Employee Intranet Software](https://www.g2.com/categories/employee-intranet) [Product Information Management (PIM) Systems](https://www.g2.com/categories/product-information-management-pim) [Quality Management Systems (QMS)](https://www.g2.com/categories/quality-management-qms) Similar Categories - [Active Learning Tools](/categories/active-learning-tools) - [Agentic AI](/categories/agentic-ai) - [AI Avatar Generators](/categories/ai-avatar-generators) - [AI Gateways](/categories/ai-gateways) - [AI Governance Tools](/categories/ai-governance-tools) - [AI Note-Taking Software](/categories/ai-note-taking-software) - [AI Orchestration](/categories/ai-orchestration) - [AI Proposal Generator Tools](/categories/ai-proposal-generator-tools) - [AI Search Visibility Optimization Tools](/categories/ai-search-visibility-optimization-tools) - [AI Security Posture Management (AI-SPM) Tools](/categories/ai-security-posture-management-ai-spm-tools) - [AI Security Solutions](/categories/ai-security-solutions) - [AI Storyboard Generators](/categories/ai-storyboard-generators) - [AI Voice Assistants](/categories/ai-voice-assistants) - [AI Voice Dictation](/categories/ai-voice-dictation) - [AI Writing Assistant](/categories/ai-writing-assistant) [Browse Data Labeling Themes](/categories/data-labeling/themes) ![Bijou Barry](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Bijou Barry") BB Researched and written by [Bijou Barry](https://research.g2.com/insights/author/bijou-barry) Updated April 9, 2026 Data labeling software helps data science and machine learning teams source, manage, annotate, and classify unstructured data, including text, images, videos, audio, and PDFs, into labeled datasets that create efficient training data pipelines for building and improving AI and ML models. ### Core Capabilities of Data Labeling Software To qualify for inclusion in the Data Labeling category, a product must: - Integrate a managed workforce and/or data labeling service - Ensure labels are accurate and consistent - Give the user the ability to view analytics that monitor the accuracy and speed of labeling - Allow annotated data to be integrated into data science and machine learning platforms to build machine learning models ### Common Use Cases for Data Labeling Software ML engineers, data scientists, and AI teams use data labeling tools to build high-quality training datasets across a wide range of application types. Common use cases include: - Annotating images, video, and text for computer vision, NLP, and speech recognition model training - Fine-tuning and evaluating large language models (LLMs) with human-labeled feedback data - Building training pipelines for object detection, named entity recognition, and sentiment analysis applications ### How Data Labeling Software Differs from Other Tools Data labeling is a foundational building block of the AI development lifecycle, distinct from the downstream tools it feeds. It integrates with [generative AI software](https://www.g2.com/categories/generative-ai), [MLOps platforms](https://www.g2.com/categories/mlops-platforms), [data science and machine learning platforms](https://www.g2.com/categories/data-science-and-machine-learning-platforms), [LLM software](https://www.g2.com/categories/large-language-models-llms), and [active learning tools](https://www.g2.com/categories/active-learning-tools) to support the full model development pipeline. ### Insights from G2 on Data Labeling Software Based on category trends on G2, labeling accuracy controls and workforce management features stand out as standout capabilities. Faster training data pipeline construction and improved model accuracy stand out as primary outcomes of adoption. Show More * * * ## How Do You Choose the Right Data Labeling Software? ### What You Should Know About Data Labeling Software ### What is Data Labeling Software? Data labeling software labels or annotates data for training machine learning models. Machine learning algorithms rely on large amounts of labeled data to learn patterns and make predictions. Data labeling solutions help humans identify and label the relevant features and characteristics of the data that will be used to train the machine learning model. Many types of data labeling solutions are available, ranging from simple tools that allow users to label data manually to more advanced tools that use machine learning algorithms to automate the labeling process. Some data labeling software also includes features such as image annotation tools, which allow users to label and annotate images and other visual data, and many data labeling platforms now extend that same tooling to computer vision tasks like bounding boxes and point cloud annotation for 3D and LiDAR data. Data labeling software is used in various applications, including[](https://www.g2.com/articles/natural-language-processing)[natural language processing,](https://www.g2.com/articles/natural-language-processing) image and video classification, and[](https://www.g2.com/articles/object-detection)[object detection](https://www.g2.com/articles/object-detection). It is an important tool in the development and training of machine learning models and plays a critical role in their accuracy and effectiveness. ### What types of data labeling software exist? Selecting a data labeling software requires a prior evaluation and understanding of data-driven workflows in your business. Below are the types of software you can consider. - **Manual labeling software:** These data labeling platforms segment, label, and classify data with the help of a "[human in the loop"](https://www.g2.com/glossary/human-in-the-loop-definition) service. Human annotators label the training data based on businesses' geographic locations. The data annotation service is extended to the[ML model](https://www.g2.com/articles/machine-learning-models) development workflow, and labeling data becomes more effective. - **Automated labeling software:** The automated data labeling software preprocesses raw datasets consisting of text, images, liDAR data, DICOM, PDF, or audio using an unsupervised learning approach. The algorithm assigns labels and categories to data without referring to external annotators. - **Active learning labeling software:** Also known as active learning tools, these are semi-supervised tools that follow a "query-based" approach to labeling data. Based on the uncertainty score, they query data using manual or annotator labeling. For more challenging labels, they prompt the human annotator with queries. - **Crowdsource labeling software:** These data labeling platforms crowd data labeling services to a crowd of developers to[train high-quality data pipelines](https://learn.g2.com/training-data). Custom data labeling can be ideal for large or enterprise-sized teams. - **Integrated labeling and model training software:** These tools provide combined services for data labeling and predictive modeling. Using advanced data analysis, users can label, train, and build machine learning models to optimize their production cycles. ### What are the Common Features of Data Labeling Software? Based on G2 reviews, data scientists and machine learning engineers evaluate data labeling software by comparing annotation tool variety, model-assisted labeling speed, and collaboration features. There are several features that are often included in data labeling software, including: - **Label assignment:** Data labeling software allows users to assign labels or tags to specific data points, such as text, images, or videos. - **Annotation tools:** Some data labeling software includes tools for annotating data, such as bounding boxes, polygon drawing tools, cloud points, keymakers, and point annotation tools. These tools can be used to highlight specific features or characteristics of the data. - **Machine learning algorithms:** Some data labeling software uses machine learning algorithms to automate the labeling process or generate initial labels for data, which humans can then review and correct as needed. - **Data management and organization** : Data labeling software often includes features for organizing and managing large datasets, such as the ability to filter and search for specific data points, track progress and completion, and generate reports. - **Collaboration tools:** Some data labeling software includes collaboration tools, such as the ability to assign tasks to multiple users, track changes and revisions, and review and discuss data labeling decisions. - **Integration with data science and machine learning platforms** : Some data labeling software is designed to integrate with popular[](https://www.g2.com/categories/data-science-and-machine-learning-platforms)[data science and machine learning platforms](https://www.g2.com/categories/data-science-and-machine-learning-platforms), such as TensorFlow or PyTorch, making it easier to use the labeled data to train machine learning models. - **Image, text, audio, or video annotation:** These tools comply with multiple unstructured data formats to train and validate models designed to generate output in images, text, video, audio, PDF, and so on. ### Benefits of Data Labeling Software Choosing a data labeling platform empowers businesses to either pre-train existing machine learning models to save time or build new models to upgrade their workflows and train teams.  While data labeling platforms can help do both, it also has some significant benefits listed as under: - **Improved accuracy and quality of labeled data** : Data labeling software can help ensure that data is accurately and consistently labeled, which is critical for the accuracy and effectiveness of machine learning models. - **Increased efficiency and productivity** : Data labeling software can help streamline the data labeling process, allowing users to label more data in less time. This can be particularly useful for large datasets or repetitive or routine tasks. - **Enhanced collaboration and team communication:** Some data labeling software includes collaboration tools, such as the ability to assign tasks to multiple users and track changes and revisions. These tools can help improve communication and coordination within teams working on data labeling projects. - **Reduced cost** : Using data labeling software can help reduce the cost of data labeling projects by automating routine tasks and reducing the need for manual labor. - **Increased flexibility and scalability** : Data labeling software can be used to label a wide variety of data types and can be easily scaled up or down as needed to meet project demands. - **Respite for data operations, ML, and data science teams:** These solutions offer agile service marketplaces with high-quality labelers and annotators that solve the problems of data cleaning, preprocessing, and classification for these teams. - **Superpixel segmentation and brushes:** These tools are also widely used for image recognition, natural language processing (NLP), and computer vision algorithms. It creates region pools using brushing and superpixel segmentation to classify images. ### Who Uses Data Labeling Software? The data labeling tools are a must-have for businesses that want to foray into AI automation and build robust and efficient product applications and SDK with pre-installed machine learning capabilities. Below are the individuals and organizations that use data labeling platforms: - **Data scientists and machine learning engineers** : Data scientists and machine learning engineers use data labeling software to label and annotate data that will be used to train machine learning models. This helps the models learn to recognize patterns and make predictions based on the labeled data. - **Business analysts and data analysts** : Business analysts and data analysts may use data labeling software to label and annotate data to create reports and visualizations or for use in machine learning models. - **Quality assurance professionals** : Quality assurance professionals may use data labeling software to label and annotate data to test and debug machine learning models or other software applications. - **Researchers** : Researchers in various fields, such as computer science, linguistics, and biology, may use data labeling software to label and annotate data to conduct research or develop machine learning models. ### Alternatives to data labeling software Some alternatives to data labeling software provide annotation and labeling services along with other machine learning features. - [Natural language processing (NLP) software](https://www.g2.com/categories/natural-language-processing-nlp) **:** The NLP software derives semantic relationships between words of an input sentence and generates relevant and personalized content. These tools replicate the functioning of a human brain to register prompt intent and derive coherent content blocks. - [Machine learning operationalization (MLOps software):](https://www.g2.com/categories/mlops-platforms) The MLOPs software facilitates the entire machine learning model journey, from data preprocessing to ML integration and delivery. It applies various DevOps automation concepts and runs ML-based workflows without human supervision. - [Image recognition software:](https://www.g2.com/categories/image-recognition) Image recognition software detects, categorizes, and localizes digital images or photographs. It is based on specialized deep-learning models that group data into grids and identify relevant categories of all objects. ### Challenges with Data Labeling Software Even though data labeling software reduces costs, provides security and privacy to data, and moderates data quality control, some evident challenges can occur at any stage of working with this platform. Below are some of the challenges of data labeling software - **Data quality and consistency:** It is not certain that data labeling tools would predict accurate labels for ML models. Sometimes, the platform can incorrectly categorize text as video or process incorrect calculations, which can lower the data quality. - **Scalability:** As a business receives large influxes of data, repurposing raw data to train models, make model versions, calculate risks, and be consistent with quality control becomes a challenge and results in scalability problems for different teams across the company. - **Cost: ** Though data labeling platforms tend to be cheaper than other expensive human annotation services, submitting a large cluster of datasets for categorization can become costly. It would exhaust your credits and leave you with no alternative but to upgrade to a more expensive plan. - **Complexity of tasks:** Not all data labeling tasks are simple. Some require deep domain exercises and more specialized algorithm training, such as reinforcement learning, query sampling, or entropy, to build ML models accurately without investing in external annotation services. - **Data privacy and security:** These platforms are open source or paid. However, they retrieve and store data on[](https://www.g2.com/categories/hybrid-cloud-storage-solutions)[hybrid](https://www.g2.com/categories/hybrid-cloud-storage-solutions) or[](https://www.g2.com/articles/public-cloud)[public cloud storage platforms](https://www.g2.com/articles/public-cloud), which can infect your dataset and give hackers and fishers leeway to infect the data.  ### What companies should buy data labeling software? Companies that want to optimize the quality of their datasets and build powerful algorithms should consider data labeling software. Not just because it helps label data but because it can build accurate predictions and forecasts. Here are some companies that can benefit from these tools: - **Machine learning startups or research labs:** These companies conduct the majority of machine learning experiments and constantly work with data tools. Investing in a data labeling tool can benefit their AI research and ML model development processes. - **Data companies:** Companies that provide data management services like search engines, e-commerce platforms, or social media management tools also need data labeling software to generate effective algorithms that generate accurate responses and deal with large data volumes. - **Market research companies:** Companies that conduct market research or gather customer insights and trends can also benefit from data labeling platforms. These platforms allow them to gather real-time market trends and track consumer behaviors. - **Healthcare organizations:** These companies utilize data labeling platforms for early detection of diseases, medical imaging, patient recordkeeping, consultation, and treatments. With this software, they accurately study patient data and forecast treatment cycles. ### How to Buy Data Labeling Software Investing in data labeling software is a step-by-step process that requires the input of all related teams and stakeholders. Below are the steps buyers need to follow chronologically to purchase the best data labeling platform for their business.  #### Requirements Gathering (RFI/RFP) for Data Labeling Software Before purchasing, buyers should consider their needs and determine what they hope to achieve with this software. Evaluate the type of database system, products, AI maturity, and budget data from revenue teams. Also, make a list of the data-related and language services you expect from the product. Enlist all these points in the form of a structured request for proposal (RFP) and get the approval of your teams and stakeholders who are involved in the decision-making process. #### Compare Data Labeling Software Products Evaluate the shortlisted products' features, security and privacy guidelines, pros and cons, pricing, and AI functionalities. Compare the features and benefits with the requirements your team has listed in the request for proposal. Analyze the budget, contract metrics, and return on investment for each software feature and compare them with those of other contenders in the market.  At this stage, buyers can also request demos or free trials to see how the software works and ensure it meets their needs. While shortlisting vendors, it is also crucial to consider their credibility. Look for vendors with a strong track record and a good reputation. #### Selection of Data Labeling Software Discuss all shortlisted software's technical and configuration workflows with your IT and software development teams. Sit with them to analyze current software consumption, active subscription plans, system of records, and IT audit reports, and then check where this software fits in your tech stack. Discuss the compatibility of the software with related account executives and sales teams to ensure that the software doesn't cause more overheads and storage expenses for your teams. #### Negotiation After finalizing the software, get your legal teams to draft a legitimate contract outlining RFP terms, renewal policies, data retention and privacy policies, and the vendor's non-compete and discuss it with the vendor. At this stage, it is also feasible to negotiate for a better subscription rate, more features, or add-ons that buyers are interested in at the vendor's discretion.  #### Final decision The final decision to purchase data labeling software lies with the buyer's decision-making teams. These could be the chief information officer (CIO), head of the data science team, or procurement team. While making this decision, it is also important to consider budget constraints, team queries, or business objectives. It will be helpful to consult with stakeholders and experts, like data scientists and ML engineers, to get their input on the best data labeling solution for the institution. ### What does data labeling software cost? The cost of data labeling software can vary widely depending on its specific features and capabilities, as well as the size and scope of the deployment. Some software is free or open-source, while others are commercial products sold on a subscription or per-use basis. Data labeling software designed for enterprise-level use with a wide range of advanced features will be more expensive than straightforward solutions. Prices can range from a few hundred dollars per year for an introductory subscription to several thousand dollars for a more comprehensive solution. It is essential to evaluate subscription, license, pay-per-seat, and pay-per-token usage costs to check whether the product is suitable for your business and has scope for a decent return on investment (ROI). While you are engaged in the monetary calculations, factor in software upgrade cost, business size, version, software maintenance, and upsell costs to indicate the budget clearly. These tools can help improve productivity and efficiency, contributing to ROI calculation. To calculate the ROI of data labeling software, the following formula can be used: ROI = (Benefits - Costs) / Costs "Benefits" is the value of the time saved and increased productivity resulting from using the software, and "Costs" is the total cost of the software license and any additional costs associated with implementation and use. ### Implementation of data labeling software When considering purchasing data labeling software, companies should have a rough vision of how to implement it for data science and machine learning teams. Other factors, such as alignment with notebook editors, statistical tools, data analysis limitations, training, and testing ML cycles, will be altered and modified per the implementation timeline of data labeling software. Below are some tips to ensure a smooth implementation. - **Integration with existing data and ML workflows:** Consult your software development teams on setting up user permissions and integrating this platform with your existing code development platform, such as R or Python editors. The first step is to ensure it is compatible with various data formats, data types, data analysis tools, and other collaborative ML tools. - **Customization and flexibility in labeling tasks:** These platforms must be agile and compatible with datasets of multiple formats and languages. It should provide customization for various tasks such as image recognition, computer vision, audio generation, video generation, and[speech recognition](https://www.g2.com/glossary/speech-recognition-definition). Labeling unstructured data should be open to anyone who authenticates their identity through multi-factor authentication and is an authorized user. - **Collaboration and workforce management features:** The data labeling platform needs to be activated for model prototype and version control. It should have features like role-based access control, data privacy and security guidelines, user authentication, model collaboration, and ML code supervision. The platform should be accessible to respective team members so they can double-check the labeled tasks and stop the model from hallucinating at any stage of the training data pipeline. - **Quality assurance and review mechanisms:** When a model's output accuracy depends on the quality of training data, it is evident that data labeling platforms need to be set of modulation accuracy, quality control, and labeling review mechanisms. Given the models might inaccurately label datasets or predict wrong values, the labels need to be further supervised by a human in the loop service or external human oracle. - **Scalability, automation, and cost efficiency:** As labeling needs grow, ML engineers and developers need to invest in a scalable and cost-efficient data labeling solution that doesn't obstruct their network infrastructure and database architecture. The final implementation step is to ensure that the controls are set, the license is active, and the platform is retrieving and labeling data typically. ### Data Labeling Software Trends Overall, these trends reflect the growing importance of data labeling in the machine learning and AI ecosystem and the need for tools and technologies to help organizations create and manage large datasets of labeled data efficiently and effectively. There are several trends surrounding data labeling software that are worth noting: - **Increased adoption of artificial intelligence (AI) and machine learning (ML)**: One key trend in data labeling software is the increasing adoption of AI and ML technologies. Many software solutions now incorporate AI and machine learning algorithms to automate and streamline the data labeling process, improving efficiency and accuracy. As with general AI software,[](https://www.g2.com/articles/ai-trends-2023)[G2 expects this software to get cheaper](https://www.g2.com/articles/ai-trends-2023). - **Growing demand for high-quality labeled data** : Another trend is the growing demand for high-quality labeled data to train and test machine learning models. Data labeling software can help organizations create and manage large datasets of labeled data, improving the quality and reliability of machine learning models. - **Focus on user experience and collaboration** : Another trend in data labeling software is a focus on user experience and collaboration. Many data labeling software solutions now offer intuitive and user-friendly interfaces, tools, and features that facilitate collaboration and teamwork. ### Data Labeling FAQs ### Most Popular FAQs #### Which data labeling software has the best reviews? Across the Data Labeling category, where ML engineers make up a notable share of the most trusted reviews, the strongest ratings tend to go to platforms that make the repetitive work of annotation feel fast and organized rather than tedious. - [Roboflow](https://www.g2.com/products/roboflow/reviews): Carries a large review base at a high rating, with users pointing to frequent updates and a genuinely friendly interface for building computer vision datasets. - [SuperAnnotate](https://www.g2.com/products/superannotate/reviews): Supports essentially every data type, with strong tools for splitting images and organizing projects across a team. - [Encord](https://www.g2.com/products/encord/reviews): A newer name in this data set, though its S3 integration and customer success team come up repeatedly as reasons teams building serious infrastructure choose it. #### What are the best automated data labeling tools? Automation here usually means pre-labeling data with a model first, so a human is verifying and correcting rather than tagging everything from a blank slate. - [Roboflow](https://www.g2.com/products/roboflow/reviews): Its data augmentation and AI labeling features come up often as a reason it saves real time over manual tagging. - [SuperAnnotate](https://www.g2.com/products/superannotate/reviews): Handles auto-labeling across a wide range of data types, which keeps a project's setup and dataset preparation in one place. - [CVAT.ai](https://www.g2.com/products/cvat-ai/reviews): A smaller sample on this specific theme, but its model-assisted annotation loop runs YOLO and SAM2 pre-annotation directly, turning most of the work into verifying rather than labeling from scratch. #### Which data labeling software enables consistent, high-quality annotations across multiple image formats and datasets? Consistency at this level usually comes down to how well a platform organizes projects and enforces the same workflow across every dataset a team touches. - [Roboflow](https://www.g2.com/products/roboflow/reviews): Lets teams export and import datasets with annotations intact, keeping formats consistent as data moves between training runs. - [SuperAnnotate](https://www.g2.com/products/superannotate/reviews): Its ability to organize projects across data types is what keeps annotation quality consistent at scale. - [Taskmonk](https://www.g2.com/products/taskmonk/reviews): A smaller sample so far, but its no-code Task Builder and pre-built quality checks are designed specifically to keep labeling consistent across any data type. #### Which data labeling software offers user-friendly interfaces and shortcut tools for efficient image annotation and tagging? The platforms reviewers single out here are the ones where annotating feels fast because of the interface itself, not just the underlying model. - [SuperAnnotate](https://www.g2.com/products/superannotate/reviews): This is the single most-mentioned strength in its reviews — a clean, intuitive interface that keeps annotation moving quickly. - [Roboflow](https://www.g2.com/products/roboflow/reviews): Described as intuitive and easy to use, with frequent updates that keep the tagging workflow current. - [FiftyOne](https://www.g2.com/products/voxel51-fiftyone/reviews): A smaller sample on this specific theme, but its visualization module lets a team spot labeling discrepancies and edge cases at a glance rather than digging through data manually. #### What is the best data labeling software? The right answer depends on the type of data and team size, but a few platforms come up across a wide range of annotation projects. - [Kili](https://www.g2.com/products/kili/reviews): A smaller footprint in this data set, but simple cloud storage integration and scalability make it work well for growing teams. - [CVAT.ai](https://www.g2.com/products/cvat-ai/reviews): Intuitive enough to find what's needed quickly, especially compared to a self-built annotation tool. - [Labelbox](https://www.g2.com/products/labelbox/reviews): A newer name here, though its professional, uncluttered interface comes up as a reason it's simple and effective to work in day to day. #### Which data labeling tools maintain stable performance and annotation quality for high-volume image batches? Volume tends to be where a platform's design choices show up the most, the same interface that feels snappy on a small project can slow down once a team is running thousands of high-resolution images through it, so reviewers' experience at scale is the most useful signal here. - [Roboflow](https://www.g2.com/products/roboflow/reviews): Reviewers describe performance as "consistently fast, very intuitive, and reliable, even when handling large datasets or heavier annotation workloads," though a few note that pagination can interrupt the review flow once a dataset gets very large. - [SuperAnnotate](https://www.g2.com/products/superannotate/reviews): Reviewers specifically call out its keyboard shortcuts as "an absolute lifesaver when working on large batches," while also noting the platform can slow down when loading very large or high-resolution datasets — a tradeoff worth weighing against its annotation depth. - [CVAT.ai](https://www.g2.com/products/cvat-ai/reviews): Its model-assisted pre-annotation loop reduces how much of a large batch needs manual tagging in the first place, which is one way to keep quality consistent as volume grows rather than relying on raw interface speed alone. #### Which data labeling platforms do ML engineers trust most for scalable annotation with intuitive design and responsive support? Engineers evaluating this category tend to weigh three things together rather than separately: does the interface stay out of the way, does support actually respond, and does the workflow speed up how fast a model can go through another training iteration. - [SuperAnnotate](https://www.g2.com/products/superannotate/reviews): One reviewer, writing from an engineering perspective, described it as reducing "coordination overhead instead of adding more complexity" and called it a "scalable, well-structured annotation solution" for machine learning projects. - [Roboflow](https://www.g2.com/products/roboflow/reviews): A research team reported cutting the time from raw imagery to a trained neural network by roughly fivefold, with reviewers separately noting that support has "always been quick and helpful" when needed. - [Labelbox](https://www.g2.com/products/labelbox/reviews): Reviewers describe a professional, uncluttered interface with "all the necessary tools" to work smoothly, without extra details getting in the way of day-to-day annotation work. ### Small Business FAQs #### What is the most affordable data labeling software for SMBs? Within the[](https://www.g2.com/categories/data-labeling/small-business)[small business segment of Data Labeling](https://www.g2.com/categories/data-labeling/small-business), the platforms that come up most often are the ones with a genuine free tier rather than just a limited trial. - [Roboflow](https://www.g2.com/products/roboflow/reviews): Offers a free entry tier, which fits its large base of solo founders and researchers using it for computer vision projects. - [CVAT.ai](https://www.g2.com/products/cvat-ai/reviews): A team running a small operation can move straight to verifying and correcting pre-annotated data rather than paying for labeling from scratch. - [SuperAnnotate](https://www.g2.com/products/superannotate/reviews): Reasonable task rates and convenient payment methods come up in reviews as reasons it works for teams watching costs closely. #### What is the best data labeling software for startups? Startups evaluating[](https://www.g2.com/categories/data-labeling/small-business)[small business data labeling](https://www.g2.com/categories/data-labeling/small-business) tools tend to prioritize a platform a lean team can run without a dedicated annotation specialist. - [Roboflow](https://www.g2.com/products/roboflow/reviews): A large share of its reviewers are founders and researchers, and its friendly, frequently updated interface fits a small team without a dedicated ML infrastructure person. - [SuperAnnotate](https://www.g2.com/products/superannotate/reviews): Handles every data type in one place, which cuts down on stitching together separate tools as a startup's dataset needs grow. - [Taskmonk](https://www.g2.com/products/taskmonk/reviews): A smaller sample so far, but its no-code Task Builder lets a startup manage labeling projects at scale without writing custom tooling. #### Which data labeling platform is the most user-friendly for startups? Ease of use matters most at this stage, since the person labeling data is often the same person building and training the model. - [SuperAnnotate](https://www.g2.com/products/superannotate/reviews): Its clean, intuitive interface is the most consistently praised feature across reviews from smaller teams. - [Roboflow](https://www.g2.com/products/roboflow/reviews): Easy to understand with a friendly UI, which shortens the ramp-up for a small team's first project. - [Datature](https://www.g2.com/products/datature/reviews): A newer name in this data set, though it's described as extremely user-friendly for both annotation and model training. #### Which data labeling tool is easiest to set up for small teams? Setup speed is one of the more differentiated ratings in this category, and small teams generally do best with a platform that's usable the same day. - [Labelbox](https://www.g2.com/products/labelbox/reviews): Setup is described as super simple with no issues, with everything accessible right after signing in. - [Segments.ai](https://www.g2.com/products/segments-ai/reviews): A smaller sample so far, but its all-in-one workflow lets a team label and review data in the same place without stitching tools together. - [Roboflow](https://www.g2.com/products/roboflow/reviews): Frequent updates and a friendly interface keep the path from signup to a first labeled dataset short. #### Which data labeling tool is best for solo researchers and independent ML practitioners? A meaningful share of this category's reviewers work alone or on very small teams, and the platforms that fit best tend to double as a full workspace rather than just a labeling tool. - [Roboflow](https://www.g2.com/products/roboflow/reviews): Its user base skews heavily toward founders and researchers working solo, with a free entry tier that matches that reality. - [FiftyOne](https://www.g2.com/products/voxel51-fiftyone/reviews): Works as a central orchestration layer for an entire computer vision pipeline, useful when one person is running evaluation, visualization, and labeling together. - [CVAT.ai](https://www.g2.com/products/cvat-ai/reviews): Built for exactly this scenario — a small operation running model-assisted pre-annotation so one person can focus on verifying rather than labeling from scratch. ### Enterprise FAQs #### What is best-rated data labeling software for large enterprises? Within the[](https://www.g2.com/categories/data-labeling/enterprise)[Enterprise segment of Data Labeling](https://www.g2.com/categories/data-labeling/enterprise), a smaller set of platforms have the review volume from large organizations to back up a strong rating. - [SuperAnnotate](https://www.g2.com/products/superannotate/reviews): Holds up well at the enterprise level, with the same broad data-type support and project organization that smaller teams rely on scaling cleanly. - [Roboflow](https://www.g2.com/products/roboflow/reviews): The same frequent updates and friendly interface that made it popular with smaller teams carry over at enterprise scale. - [Taskmonk](https://www.g2.com/products/taskmonk/reviews): A smaller footprint at this scale so far, but its custom workflows and pre-built quality checks are built to optimize labeling budgets as volume grows. #### What is the most reliable data labeling tool for enterprises? Reliability at this scale tends to come down to support responsiveness, since large teams need fast answers when an annotation pipeline stalls. - [Roboflow](https://www.g2.com/products/roboflow/reviews): Consistent support ratings back up its reputation for frequent updates and a stable, well-maintained platform. - [SuperAnnotate](https://www.g2.com/products/superannotate/reviews): Support quality holds up even as reviewers' organizations get larger and more projects get added. - [Encord](https://www.g2.com/products/encord/reviews): A smaller sample so far, but its customer success team is described as coming to every meeting prepared and actively building features based on customer feedback. #### What is best-reviewed data labeling software for enterprise-scale annotation consistency? Enterprise deployments need annotation quality to hold steady across many projects and contributors, not just within a single dataset. - [SuperAnnotate](https://www.g2.com/products/superannotate/reviews): Its project organization tools are what let large teams keep annotation standards consistent across many datasets at once. - [Roboflow](https://www.g2.com/products/roboflow/reviews): Consistent dataset export and import keeps annotation formats stable as a large organization scales up its computer vision work. - [Kili](https://www.g2.com/products/kili/reviews): A smaller footprint at enterprise scale so far, but its cloud storage integrations and scalability are what growing organizations lean on most. #### Which data labeling platform offers the strongest quality assurance and model-assisted annotation for enterprise teams? At enterprise volume, quality assurance needs to be built into the workflow itself rather than handled through manual spot-checks after the fact. - [CVAT.ai](https://www.g2.com/products/cvat-ai/reviews): Its model-assisted annotation loop runs pre-annotation directly through the platform, turning most enterprise labeling work into verification rather than labeling from scratch. - [Taskmonk](https://www.g2.com/products/taskmonk/reviews): Pre-built quality checks and custom workflows are designed specifically to enhance data quality at the volume large teams operate at. - [SuperAnnotate](https://www.g2.com/products/superannotate/reviews): Its ability to organize projects and split data across large teams supports the kind of structured QA process enterprise labeling needs. #### Which data labeling platform is best for large-scale crowdsourced annotation workforces? Some enterprise labeling needs go beyond software alone and require an actual workforce of annotators, which changes what "best" means for this question. - [Appen](https://www.g2.com/products/appen/reviews): A robust data collection and crowdsourcing platform built specifically for AI, language, and similar large-scale annotation work. - [Outlier AI](https://www.g2.com/products/outlier-ai-outlier-ai/reviews): Offers a flexible range of real AI project work, from writing prompts to reviewing responses, which enterprise teams tap into for large annotation volumes. - [Keymakr](https://www.g2.com/products/keymakr/reviews): A smaller sample so far, but its team is described as fast, responsive, and proactive about finding solutions when annotation issues come up. _Researched and written by_ [_Matthew Miller_](https://learn.g2.com/author/matthew-miller)