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
Commit History
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align weights and inference with thesis
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Update model card
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clarify where the loader goes
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add thesis checkpoints, results and model card
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initial commit
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