Train a YOLOv4-tiny Model on a Custom Dataset

YOLOv4-tiny, a streamlined version of the YOLOv4 object detection model, offers significant speed advantages in both training and inference, making it especially useful for those with limited computational resources. While it trades off some accuracy for this increased speed, it remains effective for many tasks, particularly in cases involving smaller custom datasets. The article provides a detailed guide on setting up a training environment using Google Colab, configuring the model for custom datasets, and executing the training process, emphasizing the ease of use provided by platforms such as Roboflow. It highlights the architectural differences between YOLOv4-tiny and its larger counterpart, noting the reduced number of layers and anchor boxes. The guide also touches on the importance of adjusting training parameters based on dataset characteristics and monitors the mean average precision to avoid overfitting. After training, the model can quickly infer on test images, and the weights can be exported for further applications without the need for retraining.