models/cnn/CNN_MODEL/README.md

Eye Open / Closed Classifier (YOLOv11-CLS)

Binary classifier: open vs closed eyes.
Used as a baseline for eye-tracking, drowsiness, or focus detection.

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Model team task

• Train the YOLOv11s-cls eye classifier in a separate notebook (data split, epochs, GPU, export best.pt).
• Provide trained weights (best.pt) for this repo’s evaluation and inference scripts.

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Repo contents

• notebooks/eye_classifier_colab.ipynb — Data download (Kaggle), clean, split, undersample, evaluate (needs best.pt from model team), export.
• scripts/predict_image.py — Run classifier on single images (needs best.pt).
• scripts/webcam_live.py — Live webcam open/closed (needs best.pt + optional weights/face_landmarker.task).
• scripts/video_infer.py — Run on video files.
• scripts/focus_infer.py — Focus/attention inference.
• weights/ — Put best.pt here; face_landmarker.task is downloaded on first webcam run if missing.
• docs/ — Extra docs (e.g. UNNECESSARY_FILES.md if present).

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Dataset

• Source: Kaggle — open/closed eyes
• The Colab notebook downloads it via kagglehub; no local copy in repo.

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Weights

• Put best.pt from the model team in weights/best.pt (or runs/classify/runs_cls/eye_open_closed_cpu/weights/best.pt).
• For webcam: face_landmarker.task is downloaded into weights/ on first run if missing.

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Local setup

pip install ultralytics opencv-python mediapipe "numpy<2"

Optional: use a venv. From repo root:

• python scripts/predict_image.py <image.png>
• python scripts/webcam_live.py
• python scripts/video_infer.py (expects 1.mp4 / 2.mp4 in repo root or set VIDEOS env)
• python scripts/focus_infer.py

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Project structure

├── notebooks/
│   └── eye_classifier_colab.ipynb   # Data + eval (no training)
├── scripts/
│   ├── predict_image.py
│   ├── webcam_live.py
│   ├── video_infer.py
│   └── focus_infer.py
├── weights/                     # best.pt, face_landmarker.task
├── docs/                        # extra docs
├── README.md
└── venv/                        # optional

Training and weight generation: model team, separate notebook.