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
File size: 101 Bytes
872b341 | 1 2 3 4 5 6 7 | torch==2.2.2 torchvision==0.17.2 timm==1.0.26 numpy>=1.24,<2 Pillow>=10,<12 huggingface_hub>=0.25,<1 |