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README.md

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-license: mit
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+---
+license: mit
+language:
+- en
+---
+# Fight/Violence Detection in Videos Using 3D CNN
+
+This repository contains a dataset and a 3D CNN (Convolutional Neural Network) model trained to detect fights/violence and non-violence in videos. The model is designed to capture temporal and spatial features to identify violent activities, making it suitable for real-time surveillance and security applications.
+
+## Dataset Overview
+
+- **Dataset Classes:** The dataset consists of two classes:
+  1. **Violence/Fight:** Videos where physical violence is present.
+  2. **NoViolence/NoFight:** Videos with no physical confrontations.
+  
+- **Data Format:**
+  - The dataset contains videos that are labeled into the above two classes.
+  - These videos are preprocessed and split into frames that are fed into the 3D CNN model for training and detection.
+
+## Model
+
+- **3D CNN Architecture:**
+  - The 3D CNN model is trained to detect patterns across both spatial and temporal dimensions, making it ideal for analyzing video sequences.
+  - The model uses 3D convolutional layers to capture motion and action-based features, which are crucial for fight/violence detection.
+
+## Purpose
+
+The model is developed to detect violent actions in video footage. This system can be deployed in surveillance cameras, security systems, or any environment where fight/violence detection is necessary.
+
+### Key Features:
+- **Fight/Violence Detection:**
+  - The 3D CNN model is trained to recognize fight/violence events in videos, differentiating them from non-violent actions.
+  - The model processes video sequences to make predictions, utilizing temporal changes and spatial context.
+
+## Code and Usage Instructions
+
+### Pre-requisites:
+- Python 3.8 or higher
+- TensorFlow or PyTorch (depending on the implementation)
+- OpenCV
+- FFmpeg (for video preprocessing)
+- Required libraries as mentioned in `requirements.txt`
+
+### Video Preprocessing:
+
+1. **Extract Frames from Video:**
+   The 3D CNN model expects the input as video frames. You can extract frames from videos using the following command:
+   
+   ```bash
+   ffmpeg -i <input-video> -vf fps=25 <output-frame-directory>/frame_%04d.jpg