Image Classification
PyTorch
timm
facial-expression-recognition
driver-monitoring
vision-transformer
parameter-efficient-fine-tuning
lora
adaptformer
Instructions to use headless-start/parameter-efficient-dfer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use headless-start/parameter-efficient-dfer with timm:
import timm model = timm.create_model("hf-hub:headless-start/parameter-efficient-dfer", pretrained=True) - Notebooks
- Google Colab
- Kaggle
Download stage1/best.ckpt from headless-start/parameter-efficient-dfer: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/headless-start/parameter-efficient-dfer/resolve/main/stage1/best.ckpt
- Command line
-
hf download hf://headless-start/parameter-efficient-dfer/stage1/best.ckpt
-
curl -L -o best.ckpt https://huggingface.co/headless-start/parameter-efficient-dfer/resolve/main/stage1/best.ckpt
343 MB
- Xet hash:
- 517235d86257141d88dcdfdb59f2cfff5a36d943e9c2a185eb95658226e120d2
- Size of remote file:
- 343 MB
- SHA256:
- 3094f103c17bd13558f960c22b91ed3316679cadeab88ba269d862019c4dd58a
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