Instructions to use mwalmsley/baseline-encoder-classification-resnet50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use mwalmsley/baseline-encoder-classification-resnet50 with timm:
import timm model = timm.create_model("hf-hub:mwalmsley/baseline-encoder-classification-resnet50", pretrained=True) - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from mwalmsley/baseline-encoder-classification-resnet50: direct link, hf CLI and curl.
- Browser
- Download file 94.4 MB
-
https://huggingface.co/mwalmsley/baseline-encoder-classification-resnet50/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://mwalmsley/baseline-encoder-classification-resnet50/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/mwalmsley/baseline-encoder-classification-resnet50/resolve/main/pytorch_model.bin
94.4 MB
- Xet hash:
- 7dfbb89c3983cbfe87d72ee53ef204ff7ea1c9d946b8934fddd1aa63588ebdb3
- Size of remote file:
- 94.4 MB
- SHA256:
- ed3f59e5577da683c073f4cffc2fdc6f5d7007ebfc76811280cfc8ab3351b836
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