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---
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license: mit
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---
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# Backbones
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Backbone networks exported as PyTorch programs.
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All models were pretrained on ImageNet and subsequently finetuned on a specific dataset.
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Annotation data was converted for multi-label classification where applicable.
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## Usage
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Simply load the model in PyTorch and run inference to get a mapping of features.
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```python
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import torch
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import torch.export
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image = torch.randn((1, 3, 256, 256), dtype=torch.float32, requires_grad=False)
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model = torch.export.load("resnet/resnet50-imagenet.pt2")
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model.eval()
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feats = model(image)
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assert isinstance(feats, dict), type(feats)
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assert feats["ext1"]
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assert feats["ext2"]
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assert feats["ext3"]
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assert feats["ext4"]
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```
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