Hi there!
Excellent code and am enjoying learning about it. If I want to fine-tune your pre-trained network and identify 18 key points like the normal model, should I train with annotations of only 17 key points? I don't see a need to change the keypoint set up for my goals, but I want to fine-tune just a bit for the data I'm using. So if so, should I annotate with 17 key points or 18?
I began fine-tuning by using the --weights-only flag with just ~250 images to test that are annotated with 18 keypoints. The loss is bouncy, but doesn't appear to be too high compared to what others have show. But when I validate I get an AP and AR of 0 and am having trouble figuring out what is going wrong in training. Is it that I need to make some code changes if it is 18 key points / should I just annotate with 17 and have the model detect the neck in the same manner it does during its original training with coco.
Additionally, I tried visualizing the heatmaps for debugging as they did here #235 and all of it looks correct, so I'm not sure what in the training is having issues.
Hi there!
Excellent code and am enjoying learning about it. If I want to fine-tune your pre-trained network and identify 18 key points like the normal model, should I train with annotations of only 17 key points? I don't see a need to change the keypoint set up for my goals, but I want to fine-tune just a bit for the data I'm using. So if so, should I annotate with 17 key points or 18?
I began fine-tuning by using the --weights-only flag with just ~250 images to test that are annotated with 18 keypoints. The loss is bouncy, but doesn't appear to be too high compared to what others have show. But when I validate I get an AP and AR of 0 and am having trouble figuring out what is going wrong in training. Is it that I need to make some code changes if it is 18 key points / should I just annotate with 17 and have the model detect the neck in the same manner it does during its original training with coco.
Additionally, I tried visualizing the heatmaps for debugging as they did here #235 and all of it looks correct, so I'm not sure what in the training is having issues.