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Computer Vision

Roboflow

Train and deploy custom vision models from photos

Freemium - from FreeRuns in the cloud

Pricing

Freemium - from Free

Free

Platforms

Web

Hosted app

Category

Computer Vision

58 tools

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Last updated

Sep 23

Listing updated

Best for

Label and version image datasets

Annotate with boxes or masks, track dataset versions, and manage review before a single training run starts.

Pricing checked Sep 2026

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About Roboflow

Roboflow is an end-to-end platform for custom computer vision: teams upload images, annotate them with bounding boxes or segmentation masks, augment the dataset, then train detection, classification, or segmentation models and deploy them through a hosted API, edge devices, or SDKs. Shared workspaces, version-controlled datasets, and annotation review make it usable by real teams rather than solo hobbyists.

The open-source side is substantial: the Supervision library for video processing and support for exporting to formats other frameworks consume. Deployment spans cloud APIs, NVIDIA Jetson, Raspberry Pi, and webcams through Roboflow Inference. A free public tier covers small projects, with paid plans scaling by usage. It suits product teams, students, and enterprises shipping vision features; researchers pushing novel architectures will still train with PyTorch directly.

  • Covers the full loop: annotation, training, evaluation, and deployment
  • Strong free tier for public projects and students
  • Runs on many devices and cameras, plus a hosted API fallback
  • Active open-source ecosystem, including the Supervision library
  • Training very large or experimental models still requires your own stack
  • Pricing on private projects scales with usage and needs monitoring
  • Annotation at serious volume still costs real human hours

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Programs that detect, classify, and interpret visual data, from pretrained labeling APIs you can call this afternoon to annotation and training platforms for custom models. Listings flag deployment targets (cloud, edge, on-prem), accuracy baselines, and data-privacy posture.

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