Object detection using the YOLO (You Only Look Once) algorithm is a popular deep-learning approach that allows for real-time, efficient, and accurate detection of multiple objects in an image or video stream. YOLOv7.cfg and YOLOv3.weights are specific configurations and pre-trained weights for two versions of the YOLO algorithm.
YOLOv7.cfg: YOLOv7 is an advanced version of the YOLO algorithm that incorporates improvements and optimizations over its predecessors. The configuration file (cfg) contains the network architecture details, such as the number of layers, filters, and other hyperparameters required to build the YOLOv7 model. The YOLOv7 architecture is designed to provide better accuracy and faster processing speed compared to previous versions.
YOLOv3.weights: The YOLOv3.weights file contains pre-trained model weights obtained after extensive training on a large dataset of labeled images. These weights are crucial for initializing the YOLOv3 model, and they contain the learned parameters that allow the model to make accurate predictions during object detection.