Facial Emotion Recognition — CV Course Project

A 4-week course project that fine-tunes published FER models on RAF-DB and ships a Gradio demo.

Datasets and models

Role	Choice	Justification (from research report)
Primary dataset	RAF-DB	29,672 in-the-wild images, 7 classes, every model in the report reports on it (report.md:2186)
Backup dataset	FER-2013	Free Kaggle download, no EULA, same 7 classes (report.md:1906)
Baseline model	DAN (ResNet-18)	89.70% on RAF-DB, public weights, runs on a single T4 (report.md:252)
Stretch model	POSTER++	92.21% on RAF-DB SOTA, public weights, dual-stream CNN+Transformer (report.md:905)

Full SOTA report: research/facial-emotion-recognition/report.md.

Quickstart (Colab)

1. Open notebooks/01_colab_setup.ipynb in Google Colab.
2. Run all cells: mounts Drive, clones the DAN and POSTER_V2 reference repos into third_party/, installs requirements.txt, unpacks the dataset zip from your Drive into /content/data/.
3. For training: notebooks/02_train_dan.ipynb (Week 2) or notebooks/03_train_poster.ipynb (Week 3).
4. For analysis: notebooks/04_analysis.ipynb (Week 4).

Local demo

pip install -r requirements.txt
python app/gradio_app.py

Opens a Gradio interface on http://localhost:7860/ with image upload + webcam tabs and a model selector.

Project structure

src/         library code (datasets, models, train/eval, inference)
app/         Gradio demo
configs/     YAML hyperparameter configs
notebooks/   Colab entrypoints
scripts/     dataset preparation
tests/       pytest smoke tests
research/    SOTA report (read-only)

Workflow

This repo follows a strict change-log + commit-per-change discipline. See CLAUDE.md for the rules and CHANGES.md for the rolling log.