| # Violence Detection using Conv3D | |
| ## Model Architecture | |
| - **Type**: 3D Convolutional Neural Network (Conv3D) | |
| - **Input**: Video sequence of 16 frames, resized to 112x112. | |
| - **Structure**: | |
| - 4 Conv3D Layers with BatchNorm, ReLU, and MaxPooling. | |
| - Flatten Layer. | |
| - 2 Fully Connected Layers. | |
| - Dropout (0.5) for regularization. | |
| - **Output**: Binary Classification (Violence vs No-Violence). | |
| ## Dataset Structure | |
| The code expects a `Dataset` folder in the parent directory (or modify `DATASET_DIR` in `train.py`). | |
| Structure: | |
| ``` | |
| Dataset/ | |
| ├── violence/ | |
| │ ├── video1.mp4 | |
| │ └── ... | |
| └── no-violence/ | |
| ├── video2.mp4 | |
| └── ... | |
| ``` | |
| ## How to Run | |
| 1. Install dependencies: `torch`, `opencv-python`, `scikit-learn`, `numpy`. | |
| 2. Run `python train.py`. | |