Arbitrary style transfer using TensorFlow.js
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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)
Additionally, since it's focused specifically 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).
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4 projects | news.ycombinator.com | 24 Oct 2022