My place is 151/390, poor results if you ask me. This is my second competition in kaggle(first one is dog vs cat fun competition) and the most difficult one, but it also give me chances to try new things, like detectNet, yolo-v2, mmod of dlib(deep learning version, failed miserable for fish detection), pseudo labeling, getting more familiar with keras, learn more ensemble methods from the posts of the other kaggles(I did not apply them in this competition, will try them on next competition) etc.
This competition is hard to create a robust, local validation set, because
1 : small data, less than 4000 images but we have to classify 8 classes of objects
2 : low diversity, many images looks similar
3 : ambiguity class, even humans are hard to differentiate ALB,BET,YFT
4 : imbalance data
If the purpose of this competition is challenge the limitations of deep learning, this is a nice data set. if their ultimate
goal is create a robust system to help them detect and classify fishes, I think this data set is far from ideal,