Instructions to use mwalmsley/zoobot-encoder-efficientnet_b0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use mwalmsley/zoobot-encoder-efficientnet_b0 with timm:
import timm model = timm.create_model("hf-hub:mwalmsley/zoobot-encoder-efficientnet_b0", pretrained=True) - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from mwalmsley/zoobot-encoder-efficientnet_b0: direct link, hf CLI and curl.
- Browser
- Download file 16.3 MB
-
https://huggingface.co/mwalmsley/zoobot-encoder-efficientnet_b0/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://mwalmsley/zoobot-encoder-efficientnet_b0/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/mwalmsley/zoobot-encoder-efficientnet_b0/resolve/main/pytorch_model.bin
16.3 MB
- Xet hash:
- 0ef95acd612d6112364038a63ff2dfa4388180269a8fa15b7cf4e6e013e6b524
- Size of remote file:
- 16.3 MB
- SHA256:
- 14430a8e5e57105c28f96713b0888837a8114fb1129940b21aed96b9891b432d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.