Hi all, first off thank you for all your amazing work!
I have a question regarding model performance.
I have trained my model on ~300 frames with around 10 animals per frame.
I trained it for around 100k iterations (batch size 4, also changed the nr of iterations for the learning rate).
The model is visibly performing decently when creating a labeled video, but it does have some weird jumps and sometimes not detecting an animal when its clearly visible.
When using the evaluate_multianimal_crossvalidate function the RMSE i get is between 5-10 (test RMSE is higher than train) and the pck/rpck is quite low (up to 0.4).
I was wondering what was the importance of pck/rpck. Which target is better to optimize, rpck or rmse?
Also would a low rpck indicate undertraining? My test rmse is almost double of the train so i would assume there is overfitting instead.
Any tips on this would be very welcome! I considered to change from resnet 50 to 101 due to the multiple animals. Although not sure that would necessarily help.