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 requirements.txt from headless-start/parameter-efficient-dfer: direct link, hf CLI and curl.
- Browser
- Download file 101 Bytes
-
https://huggingface.co/headless-start/parameter-efficient-dfer/resolve/main/requirements.txt
- Command line
-
hf download hf://headless-start/parameter-efficient-dfer/requirements.txt
-
curl -L -o requirements.txt https://huggingface.co/headless-start/parameter-efficient-dfer/resolve/main/requirements.txt
101 Bytes
| torch==2.2.2 | |
| torchvision==0.17.2 | |
| timm==1.0.26 | |
| numpy>=1.24,<2 | |
| Pillow>=10,<12 | |
| huggingface_hub>=0.25,<1 | |