Practice makes progress. Therefore I continued to join Kaggle’s new competition ‘Human Protein Atlas Image Classification’ after the previous one.
I used think I could get a higher rating in image processing competition. But actually, I haven’t even entered the top half of rankings. After almost three month trials and errors, here are my rethinkings:

1. To solve the unbalanced data problem, we need to use ‘focal loss’ instead of normal cross entropy loss. I should be looking at other experts’ kernels earlier, then I could use new techniques as soon as possible.

2. To augment images, ‘lower resolution’ may be a better way than ‘mix up’

3. Try SGD and Cosine Decay, not only RMSProp

4. MobileNet may cause severe overfitting than Resnet

5. If dropout and weight-decay still can’t get better affection for regularization, what should we do? (An open question, feature engineering may be the answer)

6. Use more powerful DNN framework, such as Keras, so I can spend more time on the model itself