Object-Detection

DETR Architecture Components Backbone for Feature Extraction The initial input image is processed by a backbone, typically a pre-trained Convolutional Neural Network (CNN), such as ResNet 50 or ResNet 101, trained on the ImageNet classification task. The last pooling and classification layers are discarded to produce a feature map that captures semantic information for different regions of the image. The network stride is typically 32. The feature map output dimensions are $C \times \text{feature map height} \times \text{feature map width}$, where $C$ is the number of output channels of the last convolution layer. ...

Tennis-Analysis-System Tennis-Analysis-System is a system that analyzes tennis matches and provides player statistics and mini court visualizations and It utilizes YOLO (You Only Look Once) v8x for player detection and tracking, and fine-tunes YOLOv5 for ball detection and tracking. Additionally, a ResNet50 model is used for keypoint detection and tracking. Usage Clone the repository: 1 git clone https://github.com/Sh-31/Tennis-Analysis-System.git Install the required dependencies: 1 2 pip3 install -r requirements.txt (linux) pip install -r requirements.txt (windows) Run the main script: 1 2 python3 main.py (linux) python main.py (windows) Sample Output Datasets Ball Detection For ball detection, we used the tennis-ball-detection dataset from Roboflow. You can access it at the following link: ...