If we have done 1M iterations using ResNet50 for Pranav mouse openfield dataset which has 116 frames out of 2330 frames annotated, which is 5% of data, what measurement should we report if we are comparing different hyperparamaters for deep learning?
Considering that we don’t have groundtruth for the entire data, I am perplexed by how the goodness of annotations could be verified apart from eyeballing them.
This is basically what I had which shows training loss as far as I guess:
iteration: 999970 loss: 0.0008 lr: 0.001
iteration: 999980 loss: 0.0005 lr: 0.001
iteration: 999990 loss: 0.0009 lr: 0.001
iteration: 1000000 loss: 0.0006 lr: 0.001
Specifically, after analyzing is done and trajectories are plotted, the following result file is empty:
[jalal@goku openfield-filteredDec4-trainset95shuffle1]$ cat DLC_resnet50_openfield-filteredDec4shuffle1_1000000-results.csv ,Training iterations:,%Training dataset,Shuffle number, Train error(px), Test error(px),p-cutoff used,Train error with p-cutoff,Test error with p-cutoff [jalal@goku openfield-filteredDec4-trainset95shuffle1]$ pwd /scratch3/3d_pose/animalpose/experiments/mouse1M_resnet50_DONE/openfield-filtered/evaluation-results/iteration-0/openfield-filteredDec4-trainset95shuffle1