Learn how to create a custom traffic dataset for YOLOv4 object detection algorithm from scratch using OIDv4_ToolKit and LabelImg. Follow the step-by-step guide with code and Google COLAB notebook to train your model.

Download Darknet for Windows and Linux to train and test neural networks for object detection. See papers, source code, pre-trained models, FAQ, and more for YOLOv4, Scaled-YOLOv4, and YOLOv7.

Learn how to use Darknet to train YOLOv4, the state-of-the-art object detection model, on your own dataset. Follow the steps to configure your GPU environment, download your data, and train your custom YOLOv4 detector.

Learn how to use YOLO v4 deep learning network to detect objects in images, such as vehicles. This example shows how to configure a dataset, perform data augmentation, estimate anchor boxes, and train a YOLO v4 object detector.

This repository provides PyTorch code for YOLOv4, a fast and accurate object detection algorithm. It includes pretrained models, weights, requirements, and instructions for training and testing.