Instructions to use nateraw/resnet50-beans-dummy-sagemaker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use nateraw/resnet50-beans-dummy-sagemaker with timm:
import timm model = timm.create_model("hf-hub:nateraw/resnet50-beans-dummy-sagemaker", pretrained=True) - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from nateraw/resnet50-beans-dummy-sagemaker: direct link, hf CLI and curl.
- Browser
- Download file 94.4 MB
-
https://huggingface.co/nateraw/resnet50-beans-dummy-sagemaker/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://nateraw/resnet50-beans-dummy-sagemaker/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/nateraw/resnet50-beans-dummy-sagemaker/resolve/main/pytorch_model.bin
94.4 MB
- Xet hash:
- 90035f3b89011f0496881b21e15067d982fd0ad011fb0cd6c0031a526ed34ebe
- Size of remote file:
- 94.4 MB
- SHA256:
- acddeaca9f35f55a52064fd17279fcac6391bafca96cc8ae043720d88b9fd251
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.