Hi all - happy to share my results in using FastAI and Resnet152 and a lot of differential learning rate cycles on a cellular histo-pathology dataset - up to 100% accuracy!
I saw two papers on this dataset and noticed that in both cases, the CNN’s they were using were pretty bland and in addition, very standard practice of a simple fixed learning rate, etc. One was from summer 2018, so while relatively recent, I could see the techniques we are learning here are way ahead of the curve.
I thus took it up as a challenge to apply FastAI to it. Interestingly, I started with Resnet50 and while it got to 98% and was very stable there, it became stuck on two very similar classes and could not move beyond it. I ultimately had to restart with ResNet152.
That still took a lot of cycles with the learning rate finder and differential learning rates, and a very steady train/check learning rate/ retrain process, but I did manage to tune it to repeated 100% results and thus outdo both of the papers in accuracy by a reasonable margin.
(91-95% was their best, and in one case they oddly only tested subsets of 4 classes to get that averaged 91%, not all 20 at once).