How to Fine-Tune RF-DETR Keypoints on Custom Data

RF-DETR Keypoint is an advanced real-time transformer model designed for keypoint detection, building upon the highly regarded RF-DETR architecture used in object detection and instance segmentation. This model predicts bounding boxes and keypoint coordinates within a single forward pass, without requiring non-maximum suppression (NMS) or heatmaps, and it includes confidence scores and uncertainty ellipses for each keypoint. It is versatile, allowing for fine-tuning on various keypoint layouts for different object classes beyond the default 17 keypoints trained on the COCO dataset. The tutorial details the process of fine-tuning the RF-DETR Keypoint model for custom applications, such as detecting 33 landmarks on a basketball court using the basketball-court-detection-2 dataset. The guide walks through initializing the model, training, and evaluating it on test images, while emphasizing the importance of maintaining consistency in keypoint annotation to accurately capture spatial structures. The tutorial also highlights the options of training with Roboflow's no-code platform or using an open-source Python package, providing insights into configuring training parameters and evaluating results through visualization tools.