It’s an easy to use inference server for computer vision models. The end result is a Docker container that serves a standardized API as a microservice that you

It’s an easy to use inference server for computer vision models. The end result is a Docker container that serves a standardized API as a microservice that your application uses to get predictions from computer vision models (though there is also a native Python interface). It’s backed by a bunch of component pieces: * a server (so you don’t have to reimplement things like image processing & prediction visualization on every project) * standardized APIs for computer vision tasks (so switching out the model weights and architecture can be done independently of your application code) * model architecture implementations (which implement the tensor parsing glue between images & predictions) for supervised models that you've fine-tuned to perform custom tasks * foundation model implementations (like CLIP & SAM) that tend to chain well with fine-tuned models * reusable utils to make adding support for new models easier * a model registry (so your code can be independent from your model weights & you don't have to re-build and re-deploy every time you want to iterate on your model weights) * data management integrations (so you can collect more images of edge cases to improve your dataset & model the more it sees in the wild) * ecosystem (there are tens of thousands of fine-tuned models shared by users that you can use off the shelf via Roboflow Universe[1], it plays nicely with our other open source tools like Autodistill[2] and Supervision[3], and there is a ton of content around fine-tuning models you can use with it[4]) Additionally, since it's focused on computer vision, it has specific CV-focused features (like direct camera stream input) and makes some different tradeoffs than other more general ML solutions (namely, optimized for small, fast models that run at the edge & need support for running on many different devices like NVIDIA Jetsons and Raspberry Pis in addition to beefy cloud servers). [1] https://universe.roboflow.com https://universe.roboflow.com [2] https://github.com/autodistill/autodistill https://github.com/autodistill/autodistill [3] https://github.com/roboflow/supervision https://github.com/roboflow/supervision [4] https://github.com/roboflow/notebooks https://github.com/roboflow/notebooks