Instructions to use maurice-fp/SACBenchmark-train.densenet121.CIFAR10.13 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use maurice-fp/SACBenchmark-train.densenet121.CIFAR10.13 with timm:
import timm model = timm.create_model("hf_hub:maurice-fp/SACBenchmark-train.densenet121.CIFAR10.13", pretrained=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 24431181a867e263cb4601174f28b6e24927af245d8af03c9f7bc2a92c700ebe
- Size of remote file:
- 28.5 MB
- SHA256:
- 833671b182b082451783f76a5d5ef2400c1a2ebf8dda728e5584139b11268a77
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