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zeroshot-python
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Image classification for the masses
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zeroshot-python
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# Zeroshot (Python) Image classification for the masses ## Installation Install via pip: `pip install zeroshot` For GPU support, `pip install zeroshot[torch]` N.B. In theory ONNX supports GPU, but the restrictions on CUDA version are iffy at best, and so for easiest results just use PyTorch. If you're brave, instead `pip install onnxruntime-gpu`. ## Usage First, go to app.usezeroshot.com and create a classifier. Check out the video on the [landing page](usezeroshot.com) for an example. Then, in Python (`image` should be an RGB numpy array with channels last): ```python import zeroshot # Create the classifier and preprocessing function. classifier = zeroshot.Classifier("model-uuid-goes-here") preprocess_fn = zeroshot.create_preprocess_fn() # Run the model! prediction = classifier.predict(preprocess_fn(image)) print(f"The image is class {prediction}") ``` You can also download the classifier and save it somewhere locally so you don't need to hit the server each time. Hit "download model" in the web-app and save the json file somewhere. You can then instead do: ```python classifier = zeroshot.Classifier("/home/user/path/to/model.json") ``` ## Additional Tips * To use a GPU, install the torch backend with `pip install zeroshot[torch]` * If you are hitting issues with torch trying to run on CPU, try disabling XFormers by setting XFORMERS_DISABLED=1 in your ENV varaibles. ## Read the docs See the [docs](https://github.com/moonshinelabs-ai/zeroshot-docs/blob/main/general/getting_started.md) folder for some details on how things work under the hood. ## Get help If you need help or just want to chat, join the [Moonshine Labs Slack server](https://join.slack.com/t/moonshinecommunity/shared_invite/zt-1rg1vnvmt-pleUR7TducaDiAhcmnqAQQ) and come hang out in the #zeroshot channel.
