I’d like to ask a question or two about the ‘new’ pets notebook with grad-cam heatmaps (https://github.com/fastai/course-v3/blob/master/nbs/dl1/lesson6-pets-more.ipynb).
Why do you set image size to 352 (instead of the usual 224 or 299 typical of imagenet transfer learning)?
I tried to train with 299 imgs, and as result, the image filled just the upper-leftmost subsector in the heatmap overlay.
As I got more curious, I tried and trained with 600x600 images, expecting them to be cropped (or automatically resized). But:
- they were definitely not cropped
- they were apparently not resized to an imagenet-compatible size (at least not in the heatmap overlay, which reported a shape of 600x600).
- moreover, the feature maps were bigger accordingly, and this suggests the convnet worked internally with bigger images.
- last but not least, the accuracy of the model improved substantially (hitting 0.996 vs 0.943 with 299, same dataset, kernel restarted, new learner and databunch instantiated, same number of epochs done with identical hyperparams).