This week we focused on making small improvements to our process/ model.
We fixed our training/test set so there is a time gap between the training and test classes. Before, the last image in each training class would have been directly adjacent, time-wise, to the first image in one of the testing classes. While validating, this would mean that some of the images would be almost identical to images it has already seen in training.
We also did some of the other tests, like the overfitting test to see that the loss went to 0, and it did.
Another issue, which we posted about before, was that we were getting our training accuracy to be 0. We found this .......
acc_tra = 0#recallAcc(Fvec_tra, dsets_tra.idx_to_class) (oof)
Our goal is also to see some of the images that the network is getting wrong (what class did the network guess/ what class is it supposed to be). We're close to getting this done. Part of the code maps an arbitrary class name to a specific index ('dog' -> class 7). We were able to obtain this specific index of the expected/actual of the incorrectly labelled images. All that's left is to translate this index back into the original class names. Then we can see guessed/actual of the wrong images.
We also switched our model over to resnet50 (previously resnet18), and retrained over 10 epochs.
For next week:
- Finish up checking out mislabelled images
- Use Abby's heatmap to visualize similarity within a class (to confirm the model is 'tracking cars')