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
File size: 2,584 Bytes
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"schema_version": 1,
"study": "lr_capacity_search",
"method": "linear_probe",
"protocol": "frame_block_cv_search",
"model_seed": 42,
"split_seed": 42,
"init_from_sha256": "3094f103c17bd13558f960c22b91ed3316679cadeab88ba269d862019c4dd58a",
"fold_subset": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9
],
"smoke": false,
"search_space": {
"lr_candidates": [
0.0003,
0.001,
0.003
],
"capacity_axis": null,
"reference_capacity": {},
"capacity_candidates": null
},
"lr_selection": {
"axis": "lr",
"candidates": [
0.0003,
0.001,
0.003
],
"held_at": {},
"fold_subset": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9
],
"mean_accuracy_by_candidate": [
{
"lr": 0.0003,
"config_id": "linear_probe__lr3.0e-04",
"mean_accuracy": 0.889090909090909
},
{
"lr": 0.001,
"config_id": "linear_probe__lr1.0e-03",
"mean_accuracy": 0.9227272727272726
},
{
"lr": 0.003,
"config_id": "linear_probe__lr3.0e-03",
"mean_accuracy": 0.9327272727272726
}
],
"highest_mean_accuracy": 0.9327272727272726,
"tied_highest": [
0.003
],
"tie_rule": "highest ten-fold mean held-out accuracy, then the numerically lowest learning rate on an exact tie. Accuracy alone: no UAR, no weighted F1, no loss and no tolerance band enters it",
"selected": 0.003
},
"capacity_selection": null,
"selected": {
"config_id": "linear_probe__lr3.0e-03",
"values": {
"lr": 0.003
},
"trainable_params": 4614,
"mean_accuracy": 0.9327272727272726,
"cell_dir": "outputs/search__linear_probe__src-ferplus__seed42/cells/linear_probe__lr3.0e-03"
},
"optimism": "the configuration is selected on the same ten held-out folds whose mean accuracy is then reported, so the comparison is optimistically biased by hyperparameter selection on top of the best-epoch-on-the-held-out-block optimism every cell already carries. It is not nested cross-validation, not independent validation, not an unbiased estimate and not a like-for-like comparison with the published figures",
"design": "a sequential hyperparameter study: learning-rate selection, then capacity selection at the selected rate. The best-observed configuration of each strategy is the configuration that enters the five-method comparison",
"comparison_eligible": true,
"comparison_eligibility": "selected over all ten folds"
}
|