Some summaries for Kaggle’s competition ‘Humpback Whale Identification’

This time, I only spent one month on competition “Humpback Whale Identification”. But still, get a little step forward than previous competitions. Here are my summaries:

1. Do review ‘kernels’ in competition page, this will teach me a lot of information and new technology. By using Siamese Network rather than classic model, I eventually beat overfit problems. Thanks for suggestions from the ‘kernel’ page of competition.

2. Bravely use cutting-edge model, such as ResNeXt50 / Densenet121. They are more powerful and easy to use.

3. Do use fine-tuning. Don’t train model from scratch every time!

4. Ensemble learning is really powerful. I have used three different models to ensemble the final result.

There are also some tips for future challenge (may be correct, may be wrong):

1. albumentations is handful library for image augmentations

2. Cosine-decay-learning-rate performs worse than Exponential-decay-learning-rate

3. LeakyRelu doesn’t work significantly better than Relu

4. Bigger image size may not lead to higher accuracy

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