FixRes mitigations
An important property identified early in our experiments is the fact that the models performed significantly better if the resolution used during validation was increased from the 224x224 of training. This effect is studied in detail on the FixRes paper [5] and two mitigations are proposed: a) one could try to reduce the training resolution so that the accuracy on the validation resolution is maximized or b) one could fine-tune the model on a two-phase training so that it adjusts on the target resolution. Since we didn’t want to introduce a 2-phase training, we went for option a). This means that we reduced the train crop size from 224 and used grid search to find the one that maximizes the validation on resolution of 224x224.
Below you can see the optimal value used on our recipe:
val_crop_size=224, 
train_crop_size=176,

The above optimization improved our accuracy by an additional 0.160 points and sped up our training by 10%.