Instructions to use edadaltocg/resnet50_simclr_cifar100 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use edadaltocg/resnet50_simclr_cifar100 with timm:
import timm model = timm.create_model("hf-hub:edadaltocg/resnet50_simclr_cifar100", pretrained=True) - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from edadaltocg/resnet50_simclr_cifar100: direct link, hf CLI and curl.
- Browser
- Download file 94.3 MB
-
https://huggingface.co/edadaltocg/resnet50_simclr_cifar100/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://edadaltocg/resnet50_simclr_cifar100/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/edadaltocg/resnet50_simclr_cifar100/resolve/main/pytorch_model.bin
94.3 MB
- Xet hash:
- 99ca93060aba4b9e8fa99581a56fad2f2df32d5851a3760b81e269520ac3ef55
- Size of remote file:
- 94.3 MB
- SHA256:
- 625d4533a97864bf4107ff7e202f337bcfd3cea965d54f68a3380714338f0a21
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