Musawer14

Fight/Violence Detection Dataset and Model Overview This repository contains a dataset and YOLOv8 models (nano and small) trained to detect fights/violence and non-violence/no-fight in both videos and images. The models are optimized for surveillance and security applications where detecting physical confrontations is crucial. - Dataset Classes: The dataset consists of two classes: 1. Violence/Fight: Instances where physical violence is present. 2. NoViolence/NoFight: Instances with no physical confrontations. - Data Format: - Videos and Images are labeled accordingly for each class. - The dataset is designed for training deep learning models like YOLOv8 for violence detection. - YOLOv8 Models: - We have primarily trained YOLOv8-nano and YOLOv8-small models. - These models are lightweight and efficient, making them suitable for real-time detection tasks in resource-constrained environments. The models are trained to accurately detect violent events in various settings, including crowds, public spaces, and sports activities. Key Features: - Single Class Detection: - The attached code is specifically designed to detect one class at a time, with the focus being on the Violence/Fight class. - If the purpose is to detect only Violence/Fight, the models and code are pre-configured for this task. - Non-violence events are ignored during detection, allowing the model to concentrate solely on identifying violent actions. Pre-requisites: - Python 3.8 or higher - YOLOv8 (Ultralytics) - PyTorch - OpenCV 3. Run Single Class Detection (Violence/Fight): The provided script detects only the Violence/Fight class in both videos and images. Replace ` ` with the path to the video or image you wish to analyze. The class ID for Violence/Fight is 1. 4. Model Weights: - The `best.pt` file contains the pre-trained YOLOv8-nano or YOLOv8-small model optimized for detecting violence/fights. - Model Performance: The models are trained on a diverse set of images to generalize across different environments. However, additional fine-tuning may be required depending on your specific use case. - Future Enhancements: We plan to extend the dataset and include more diverse scenarios to improve detection accuracy, including sports, public gatherings, and more.