How to Train a YOLOv5 Model On a Custom Dataset

The post discusses the process of training a YOLOv5 object detection model on a custom dataset, using a blood cell detection dataset as an example, and provides a step-by-step guide on setting up the environment, downloading data, configuring the model, and training it. It highlights the use of Google Colab for training with a Tesla P100 GPU, the utility of Roboflow for data labeling and management, and the potential for using Autodistill to simplify model training. The tutorial also covers evaluation of the model using validation metrics, visualization of training data and results, running inference on test images, and exporting model weights for future use. Additionally, it briefly mentions the release of YOLOv8 in January 2023 and compares YOLOv5's performance with other models like YOLOv4 and EfficientDet, noting that YOLOv5 is lightweight, easy to use, and performs well. The post concludes by discussing the deployment of the trained model using Roboflow's API or inference solutions, emphasizing the ease of use and fast inference speed of YOLOv5.