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@@ -51,18 +51,20 @@ const transforms = new Compose([
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const loader = new DataLoader(new ImageFolder("./test/resources/dataset"), 1, transforms);
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const results = await squeezeNet(loader);
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```
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### More Examples
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Additional examples of both setup and usage involving features like Torchvision Transforms and CUDA (in development) may be found [here](https://github.com/raghavmecheri/ptjs/tree/master/examples).
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## Key Features
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* Run your PyTorch models in a Javascript environment, without worrying about setting up Torchscript or downloading custom binaries
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* Deploy your model using configurations identical to what you used during training
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* Built-in CUDA support
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* CUDA support is a work in progress
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* Support for TorchVision, including transforms, dataset classes, and pretrained models
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* Support for TorchVision, including transforms, dataset classes, and pre-trained models
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* Support for TorchVision models is a work in progress
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## Development
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* ```yarn install``` should allow you to install project dependancies
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* ```yarn install``` should allow you to install project dependencies
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* ```yarn test``` to run the test suite for this project
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## Misc

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