Thank you for a good annotation tools. I have tried supervisely, cvat and yours is more convinient at least for me. I have few remarks for improvement.
Before exporting labels in JSON coco or TXT YOLO v5 can you please check the coordinates of deleted keypoints and restrict it to [0…1]. Because on border of image if user shifts keypoints template it sees the visible and occluded keypoints, but users do not sees deteted keypoint. So, when we move the template to the border the deleted points also move and are outside of image. Therefore, on begin of training stage I get corrupt image label. Because, YOLOv8-pose still validates the coordinates of each keypoint even if the keypoint is marked as “visibility=0”.
train: Scanning C:\tf\yolov8\pose\person\thermal_person\train\labels… 777 images, 0 backgrounds, 1 corrupt: 100%|████
train: WARNING C:\tf\yolov8\pose\person\thermal_person\train\images\20240201144851644_01440.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0213]
So, I propose to make the deleted keypoints still visible. And the user can see and move that point on valid region of image. Or before exporting and downloading the label just check the coordinate and if it is larger that 1 then keep it i.e. 0.999 or always set to 0 if visibility=0.
Another issue is the import of labels. The roboflow recognizes the JSON format keypoints when I drop keypoint and labels. But it not recognizes the TXT YOLO v5 format, so it do not marks the keypoints.