Instructions to use mwalmsley/baseline-encoder-classification-maxvit_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use mwalmsley/baseline-encoder-classification-maxvit_base with timm:
import timm model = timm.create_model("hf-hub:mwalmsley/baseline-encoder-classification-maxvit_base", pretrained=True) - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from mwalmsley/baseline-encoder-classification-maxvit_base: direct link, hf CLI and curl.
- Browser
- Download file 463 MB
-
https://huggingface.co/mwalmsley/baseline-encoder-classification-maxvit_base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://mwalmsley/baseline-encoder-classification-maxvit_base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/mwalmsley/baseline-encoder-classification-maxvit_base/resolve/main/pytorch_model.bin
463 MB
- Xet hash:
- 2b0063fc201a24109bd3dc797f538ca39f0c7db2f012bc942d13019b283315c6
- Size of remote file:
- 463 MB
- SHA256:
- 5e30d623e8bf6dfdd2274862ff49bb98f0f224c8d9b865ef3d49481801253fa3
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