diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..8c75e348a55bdcad8e3b148f31ca2e6ca63f0991 --- /dev/null +++ b/README.md @@ -0,0 +1,145 @@ +--- +license: cc-by-nc-4.0 +library_name: timm +pipeline_tag: image-classification +base_model: timm/vit_base_patch16_224.augreg_in21k_ft_in1k +tags: + - facial-expression-recognition + - driver-monitoring + - vision-transformer + - parameter-efficient-fine-tuning + - lora + - adaptformer + - ssf + - pytorch +metrics: + - accuracy +--- + +# Parameter-Efficient Adaptation of Facial Expression Recognition Models for Driver Monitoring + +Trained weights for the MSc thesis of Ayush Tiwari, BTU Cottbus-Senftenberg, 2026. + +A ViT-B/16 (`vit_base_patch16_224.augreg_in21k_ft_in1k`) is first fine-tuned on a +six-class subset of FER+ (Stage 1). Five adaptation strategies then transfer it +to the KMU-FED in-vehicle expression corpus under ten-fold cross-validation +(Stage 2): full fine-tuning, a linear probe, SSF, LoRA and AdaptFormer. + +This repository holds every checkpoint the reported results come from: the +Stage-1 backbone and the ten fold checkpoints of each strategy's selected +configuration, 51 checkpoints in total, together with the per-fold metrics, +per-frame predictions and selection records. No image data is included. + +Classes, in output order: `angry, disgust, fear, happy, sad, surprise`. + +## Results + +### Stage 1: FER+ (six classes) + +Learning rate selected from {1e-5, 3e-5, 1e-4} on the official validation split; +the test split was evaluated once, for the selected checkpoint. + +| Learning rate | Epoch kept | Val. accuracy | Test accuracy | Test UAR | Test weighted F1 | +|---|---|---|---|---|---| +| 1e-5 | 28 | 94.36 | 93.21 | 81.40 | 93.13 | + +### Stage 2: KMU-FED, ten-fold cross-validation + +Mean over the ten held-out folds (110 frames each); ± is the standard deviation +across folds, reported as spread only. + +| Strategy | Selected configuration | Trainable parameters | Accuracy (%) | UAR (%) | Weighted F1 (%) | +|---|---|---|---|---|---| +| LoRA | lr 1e-3, rank 4 | 152,070 (0.177%) | 99.00 ± 2.59 | 98.56 | 98.83 | +| AdaptFormer | lr 1e-3, bottleneck 64 (reduction factor 12) | 1,194,246 (1.373%) | 98.45 ± 2.43 | 98.21 | 98.40 | +| SSF | lr 3e-3 | 210,438 (0.245%) | 97.91 ± 2.88 | 97.48 | 97.75 | +| Full fine-tuning | lr 3e-5 | 85,803,270 (100%) | 97.55 ± 4.11 | 96.87 | 97.25 | +| Linear probe | lr 3e-3 | 4,614 (0.005%) | 93.27 ± 6.83 | 92.76 | 92.42 | + +### How to read these numbers + +- Folds are contiguous per-class blocks of frames, so the same drivers appear in + training and test. The results describe familiar drivers, not unseen ones. +- Each fold keeps the epoch with the highest accuracy on its own held-out block, + and each strategy's configuration was chosen by mean accuracy over the same ten + folds. Both choices make the estimates optimistic. There is no separate + validation split at Stage 2. +- One seed (42) was used throughout. + +## Repository layout + +```text +stage1/ + best.ckpt full ViT-B/16 fine-tuned on FER+ (sha256 3094f103c17bd135...) + results.json validation history and test metrics + study.json the three learning-rate candidates and the selection rule +stage2// + selection.json every candidate's ten-fold mean accuracy and the selected configuration + search_summary.json summary of the strategy's search + fold_00 ... fold_09/ + best.ckpt retained checkpoint for that fold + results.json metrics, per-class recall, confusion matrix, training history + predictions.json label, prediction and probabilities for every held-out frame +load_weights.py loads any checkpoint into the model of the code release +``` + +`` is one of `full_ft`, `linear_probe`, `ssf`, `lora`, `adaptformer`. +The Stage-1 and full fine-tuning checkpoints contain the whole network (343 MB). +The linear probe, SSF, LoRA and AdaptFormer checkpoints contain only the +trained parameters and the classification head (0.02 to 4.8 MB), which are +applied on top of the Stage-1 encoder. + +## Usage + +The checkpoints are PyTorch files with the weights under the `model` key. +`load_weights.py` builds the matching model with `methods.py` from the thesis +code release, so copy it next to that code: + +```python +from load_weights import load_stage2 + +model = load_stage2("lora", fold=0) # downloads stage1/best.ckpt as well +``` + +Inputs are face crops converted to three-channel grayscale, resized to 224 x 224 +with bicubic interpolation and normalised with mean and standard deviation 0.5 +(`build_transform(False, kmufed=True)` in `data.py`). KMU-FED frames were cropped +offline with MTCNN before training. + +The loader was checked against the saved predictions: for every strategy and +every fold it reproduces the stored prediction for all 110 held-out frames. + +Checkpoints are pickled PyTorch files and need `torch.load(..., weights_only=False)`; +load them only from this repository. + +## Training summary + +| | Stage 1 | Stage 2 | +|---|---|---| +| Data | FER+ six-class subset: 17,651 train / 2,004 val / 2,047 test | KMU-FED, 1,106 frames; 996 train / 110 test per fold | +| Labels | official FER+ cross-entropy targets (train), majority vote (evaluation) | hard labels | +| Epochs | 120 | 60 per fold | +| Optimiser | AdamW, weight decay 0.05, 2 warmup epochs then cosine to 1e-7 | same | +| Batch, precision | 32, mixed precision | same | +| Augmentation | grayscale flip, rotation ±15°, translation, scale 0.90-1.10, shear, blur, erasing | same, rotation ±30° and scale 0.75-1.15 | +| Search | learning rate | learning rate, then rank (LoRA) or bottleneck width (AdaptFormer) at the selected rate | + +## Data and licence + +The weights are released for non-commercial research under CC BY-NC 4.0. They +were trained on FER+ (FER2013 images with the Microsoft FER+ labels) and on +KMU-FED, which is available from its authors on request; the terms of those +datasets and of the pretrained ViT checkpoint continue to apply. The Stage-2 +models were trained and evaluated on a small number of drivers and are not +suitable for any decision about a real person. + +## Citation + +```bibtex +@mastersthesis{tiwari2026peft_dfer, + author = {Ayush Tiwari}, + title = {Parameter-Efficient Adaptation of Facial Expression Recognition Models for Driver Monitoring}, + school = {Brandenburgische Technische Universit{\"a}t Cottbus-Senftenberg}, + year = {2026} +} +``` diff --git a/load_weights.py b/load_weights.py new file mode 100644 index 0000000000000000000000000000000000000000..637949c1de01ad2c0cad27ec1e1afca75384330e --- /dev/null +++ b/load_weights.py @@ -0,0 +1,62 @@ +"""Load a released checkpoint into the model defined by the code release's methods.py. + +Place this file next to methods.py, experiment.py, data.py and checkpoint.py. +""" +import re + +import torch +from huggingface_hub import hf_hub_download + +import methods + +REPO_ID = "headless-start/parameter-efficient-dfer" +CLASS_NAMES = ["angry", "disgust", "fear", "happy", "sad", "surprise"] + + +def _rename(name): + # The training runs used older parameter names than the code release. + name = re.sub(r"\.ssf_gamma$", ".gamma", name) + name = re.sub(r"\.ssf_beta$", ".beta", name) + for old, new in ((".lora_q_A.", ".q_a."), (".lora_q_B.", ".q_b."), + (".lora_v_A.", ".v_a."), (".lora_v_B.", ".v_b."), + (".adaptmlp.", ".adapter.")): + name = name.replace(old, new) + return name + + +def _state(path): + return torch.load(path, map_location="cpu", weights_only=False)["model"] + + +def load_stage1(): + model = methods.build_model(pretrained=False) + model.load_state_dict(_state(hf_hub_download(REPO_ID, "stage1/best.ckpt"))) + return model.eval() + + +def load_stage2(method, fold): + """method: full_ft, linear_probe, ssf, lora or adaptformer; fold: 0-9.""" + file = f"stage2/{method}/fold_{fold:02d}/best.ckpt" + state = {_rename(k): v for k, v in _state(hf_hub_download(REPO_ID, file)).items()} + model = methods.build_model(pretrained=False) + if method != "full_ft": + # Adaptation weights sit on top of the frozen Stage-1 encoder. + backbone = _state(hf_hub_download(REPO_ID, "stage1/best.ckpt")) + model.load_state_dict({k: v for k, v in backbone.items() + if not k.startswith("head.")}, strict=False) + methods.configure(model, method, rank=4, reduction=12) + _, unexpected = model.load_state_dict(state, strict=method == "full_ft") + if unexpected: + raise ValueError(f"unexpected keys in {file}: {unexpected[:3]}") + return model.eval() + + +if __name__ == "__main__": + import sys + from PIL import Image + from data import build_transform + + model = load_stage2("lora", 0) + image = build_transform(False, kmufed=True)(Image.open(sys.argv[1]).convert("RGB")) + with torch.no_grad(): + print(CLASS_NAMES[model(image[None]).argmax(1).item()]) diff --git a/stage1/best.ckpt b/stage1/best.ckpt new file mode 100644 index 0000000000000000000000000000000000000000..99e089c41304e1caf06d591d0fd438dc58eac14c --- /dev/null +++ b/stage1/best.ckpt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3094f103c17bd13558f960c22b91ed3316679cadeab88ba269d862019c4dd58a +size 343272834 diff --git a/stage1/results.json b/stage1/results.json new file mode 100644 index 0000000000000000000000000000000000000000..d57c2ed7efbbe4395a90268a3e2dd314f745de0d --- /dev/null +++ b/stage1/results.json @@ -0,0 +1,1368 @@ +{ + "best_val_war": 0.9436127744510978, + "best_val_uar": 0.8422065711699508, + "best_val_loss": 0.16450884051903517, + "best_epoch": 28, + "epoch_selection": { + "rule_id": "set_rule_war_then_uar_v1", + "rule": "retain every candidate whose validation WAR is within 1e-4 of the global maximum; within that set retain every candidate whose validation UAR is within 1e-4 of the retained maximum; within that, every candidate whose validation loss is within 1e-4 of the retained minimum; break remaining ties with the declared ordered keys. 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cell is optimistic before selection adds its own bias", + "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", + "one model seed and no replicates, so no cell-to-cell difference carries a statistical claim", + "fold subset: all 10 folds" + ] +} \ No newline at end of file diff --git a/stage2/ssf/selection.json b/stage2/ssf/selection.json new file mode 100644 index 0000000000000000000000000000000000000000..bd2709ff0148da275862061312cd5de62ed1a64c --- /dev/null +++ b/stage2/ssf/selection.json @@ -0,0 +1,90 @@ +{ + "schema_version": 1, + "study": "lr_capacity_search", + "method": "ssf", + "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": "ssf__lr3.0e-04", + "mean_accuracy": 0.959090909090909 + }, + { + "lr": 0.001, + "config_id": "ssf__lr1.0e-03", + "mean_accuracy": 0.9709090909090909 + }, + { + "lr": 0.003, + "config_id": "ssf__lr3.0e-03", + "mean_accuracy": 0.9790909090909091 + } + ], + "highest_mean_accuracy": 0.9790909090909091, + "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": "ssf__lr3.0e-03", + "values": { + "lr": 0.003 + }, + "trainable_params": 210438, + "mean_accuracy": 0.9790909090909091, + "cell_dir": "outputs/search__ssf__src-ferplus__seed42/cells/ssf__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" +}