ONFIRE Contest 2023 – Early Fire Detection from Video

Lightweight, modular PyTorch pipeline for rapid wildfire / smoke onset detection in surveillance video streams. Originally built for the 2023 ONFIRE (Open Fire) Contest.

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🔥 Key Features

• Multiple CNN backbones: FireNet, FireNetV2, MobileNetV2, MobileNetV3 Small, ResNet18, ResNet50.
• Frame-level sparse temporal sampling with segment-based dataset loader.
• K-fold cross validation utilities (implemented inside train.ipynb).
• Unified metrics (precision, recall, accuracy, delay & normalized delay).
• Training / validation curve aggregation and plotting from raw TensorBoard event exports.
• Inference script (test.py) for test set video evaluation.
• CSV summarization + aggregated statistics per experiment.

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🏗 Project Structure (Refactored)

scripts/
    summary.py           # Fold aggregation (mean/std/min/max/median)
    plots.py             # fold plot generator
    to_csv.py            # event export → CSV
    converter.py         # .avi → .mp4 helper (ffmpeg wrapper)
    graphs.py            # Plot training vs validation curves from CSV logs
    metrics.py           # Evaluation utilities (callable functions)
custom_models/
    FireNet.py          # Original Keras → PyTorch port
    FireNetV2.py        # Improved FireNetV2 architecture
    models.py           # Generic builders and optimizer helpers
best_model/
    best_model.pth      # Best model weights (MobileNetV3)
train.ipynb             # End‑to‑end training workflow (data prep → K-fold training)
test.ipynb              # Inference / result generation

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🚀 Quick Start

1. Install Dependencies

python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt

2. Prepare Data

Extract frames & annotations as per contest specification. Frame extraction & dataset assembly utilities are inside the initial cells of train.ipynb (sections: download → extract → dataset build).

3. Train Models

Open train.ipynb and run sequentially. Each attempt section ("Attempt X: ModelName") defines:

• Model instantiation
• Optimizer / scheduler setup
• K-fold loop
• Logging to TensorBoard

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📊 Metrics Implemented

• TP / TN / FP / FN
• Accuracy, Precision, Recall
• Average Delay (frames)
• Normalized Average Delay (rewarding early detection)

Delay is computed only on true positives; early guesses before allowed tolerance (delta_t) count as false positives.

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🧪 K-Fold Strategy

Implemented manually inside notebook; each fold logs separate TensorBoard scalars which are post-processed into CSV → summary stats.

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📚 Dependencies

See requirements.txt for a concrete list of dependencies.