Image Classification
timm
PyTorch
facial-expression-recognition
driver-monitoring
vision-transformer
parameter-efficient-fine-tuning
lora
adaptformer
ssf
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
clarify where the loader goes
Browse files- load_weights.py +1 -1
load_weights.py
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"""Load a released checkpoint into the model defined by the code release's methods.py.
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Place this file
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"""
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import re
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"""Load a released checkpoint into the model defined by the code release's methods.py.
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Place this file in the root directory of the code release, next to methods.py.
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"""
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import re
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