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README.md

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+---
+license: mit
+tags:
+  - facial-expression-recognition
+  - action-units
+  - valence-arousal
+  - affective-computing
+  - abaw
+  - rectified-flow
+  - dinov3
+pipeline_tag: image-classification
+---
+
+# AffectFlow-DINO
+
+Uncertainty-aware multi-task facial affect estimation for the **11th ABAW Multi-Task Learning
+challenge**: DINOv3 backbone + deterministic VA/expression/AU heads + a conditional
+rectified-flow head over the joint 22-dimensional affect vector.
+
+Code, training scripts, and the full 28-experiment ablation study:
+**[github.com/Bekhouche/AffectFlow-DINO](https://github.com/Bekhouche/AffectFlow-DINO)**.
+
+## Quickstart
+
+```bash
+pip install torch torchvision transformers huggingface_hub pillow
+git clone https://github.com/Bekhouche/AffectFlow-DINO.git && cd AffectFlow-DINO
+python inference.py --model finetune-flow-retune-b10 --image face.jpg
+```
+
+## Models in this repo
+
+Each subfolder is one checkpoint: `model.pt` (weights + architecture config) plus, where
+applicable, `au_thresholds.json` and/or `expr_weights.json` post-hoc calibration files that
+`inference.py` applies automatically.
+
+| Folder | Backbone | Best decode | P_MTL (calibrated) | P_VA | P_EXPR | P_AU |
+|---|---|---|---|---|---|---|
+| `finetune-flow-retune-b10` | ViT-S/16, fine-tuned | Det | **1.177** | 0.325 | 0.350 | 0.502 |
+| `vitb-finetune` | ViT-B/16, fine-tuned | Det | 1.116 | 0.354 | 0.255 | 0.507 |
+| `finetune-flow-retune-b05` | ViT-S/16, fine-tuned | Det / Flow | 1.062 / 0.956 | 0.291 | 0.303 | 0.469 |
+| `finetune` | ViT-S/16, fine-tuned | Det | 1.101 | 0.318 | 0.285 | 0.497 |
+| `au-posw-cw` | ViT-S/16, frozen | Det | 0.890 (no cal.) | 0.211 | 0.240 | 0.439 |
+| `psp` | ViT-S/16, frozen (patch soft-pool) | Det | 0.859 (no cal.) | 0.235 | 0.236 | 0.389 |
+| `affectflow-base` | ViT-S/16, frozen | Flow | 0.888 | 0.237 | 0.210 | 0.441 |
+| `det-baseline` | ViT-S/16, frozen | Det | 0.793 (no cal.) | 0.199 | 0.216 | 0.378 |
+
+`P_MTL = P_VA + P_EXPR + P_AU` is the official ABAW MTL composite metric (P_VA = mean CCC of
+valence/arousal, P_EXPR = expression macro-F1, P_AU = mean AU F1), evaluated on the s-Aff-Wild2
+validation split. Official challenge baseline: P_MTL = 0.450.
+
+These 8 checkpoints are a curated subset of the ~30 ablation runs behind the paper — one per
+architecturally distinct configuration. The full ablation table, including sweeps that reused
+one of these architectures with different loss weights/regularization, is in the GitHub repo's
+[EXPERIMENTS.md](https://github.com/Bekhouche/AffectFlow-DINO/blob/main/EXPERIMENTS.md).
+
+## Input / output
+
+- Input: a cropped, roughly frontal face image, resized to 224x224 and ImageNet-normalized
+  (handled automatically by `inference.py`).
+- Output: valence, arousal in `[-1, 1]`; one of 8 expressions (Neutral, Anger, Disgust, Fear,
+  Happiness, Sadness, Surprise, Other); a subset of 12 active Action Units (AU1, AU2, AU4, AU6,
+  AU7, AU10, AU12, AU15, AU23, AU24, AU25, AU26).
+- Two decode modes: `deterministic` (task heads directly) or `flow` (average of N sampled
+  rectified-flow trajectories — enables uncertainty estimation via trajectory spread).
+
+## Limitations
+
+Trained and validated only on s-Aff-Wild2 (in-the-wild but frame-level, no temporal context).
+Expression prediction remains the weakest task (P_EXPR <= 0.35): Fear and Sadness are rare
+classes that only partially recover under calibration. Not evaluated on demographic subgroups;
+treat outputs as research signals, not clinical or high-stakes decisions.
+
+## Citation
+
+```bibtex
+@inproceedings{affectflowdino2026,
+  title     = {AffectFlow-DINO: Uncertainty-Aware Multi-Task Affect Estimation via Conditional Rectified Flow},
+  author    = {Bekhouche, Salah},
+  booktitle = {11th Workshop on Affective Behavior Analysis in-the-wild (ABAW), ECCV},
+  year      = {2026}
+}
+```
+
+## License
+
+MIT.