This is a follow up question from this topic: https://forum.image.sc/t/multi-animal-identity-switch/44032. I think I should start a new topic so more people can see.
I have switched to normal DLC and labeled 200 frames, where body parts are defined as “monkey1_nose, monkey2_nose" … "monkey1_tail base, … " as suggested above. I have 2 monkeys in the same cage in each video, and each monkey has 31 body parts, so I have 62 body parts in total.
Right now I have an issue with the performance of the model. When I was using maDLC (also with about 200 frames and 31 body parts), the model converged pretty fast (around 100k iterations) with a pretty good training error (around 3px). But with normal DLC, the loss won’t reach a plateau even after 600k iterations, and the training error is pretty high (~6.7 px), though I understand that normal DLC has different setup comparing to maDLC that it does require more iterations for the network to converge.
- Is there a similar way to visually check the accuracy of the predictions in normal DLC, as with create_video_with_all_detections or extract_outlier_frames in maDLC?
The images created in the evaluate_network doesn’t give me the general picture, because they are cropped into small pieces.
- In maDLC, the Euclidean distance statistics per bodypart (in pixels) is listed after running evaluate_network. Is there any way to do this with normal DLC, so I can find out which body parts are causing the errors?
- Should I reduce the number of body parts? I’m feeling maybe too many body parts is increasing the difficulty for the network to make predictions.
- Any other suggestions on how to improve the performance in general, specifically on using normalDLC to track multiple animals? I don’t find too much info online regarding this topic.