It’s worth noting that the FixRes effect still persists, meaning that the model continues to perform better on validation when we increase the resolution. Moreover, further reducing the training crop-size actually hurts the accuracy. This intuitively makes sense because one can only reduce the resolution so much before critical details start disappearing from the picture. Finally, we should note that the above FixRes mitigation seems to benefit models with similar depth to ResNet50. Deeper variants with larger receptive fields seem to be slightly negatively affected (typically by 0.1-0.2 points). Hence we consider this part of the recipe optional. Below we visualize the performance of the best available checkpoints (with the full recipe) for models trained with 176 and 224 resolution:

