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kaggle-fisheries-steller-sea-lion

Our solution for Kaggle competetion NOAA Fisheries Steller Sea Lion count, big thanks to the authors of all kernels & posts, which were of great inspiration.
Kaggle Profile : Daft Vader | Charles Jansen

Methodology

Our LB result came from a CNN done in Keras.

In this script we do the following steps:

  1. Resizing: we resized all the images to 512 x 512. (We tried the same with a resize of 1024 x 1024, but with lower results)

  2. Creating more data: we used the ImageDataGenerator from Keras to create more data with horizontal and vertical flips.

  3. Architecture: we follow the following architecture:

    • Input
    • Conv2D 32, 3, 3, same
    • Relu
    • MaxPooling 2D 2, 2
    • Conv2D 64, 3, 3 same
    • Relu
    • MaxPooling 2D 2, 2
    • Conv2D 128, 3, 3, same
    • Relu
    • MaxPooling 2D 2, 2
    • GlobalAveragePooling2D

We did not use Dense layers at the end.

We tried to use Dropout, and Batch Normalization, alone or together, but it would make us overfit each time.

If we had more time we would have used the same approach on patches (512 x 512) of the pictures instead of doing it on a resized picture only.

Experiments

Various experiments were tried in order to solve this problem, a breif overview is given below with the respective notebooks.

  1. Segmentation: Segmentation is applied to the image to predict small squares centered on the coordinates of the dots. Tiramisu network is used with 1:1 upsampling to predict 6 classes(5 types of sea lions and background), Log loss is used with Nadam optimizer for training. Results with this approach were not satisfactory, the model was biased towards predicting the background.

  2. Masking with segmentation: A mask is created to focus the predictor on the areas where sea lions might be present using the same segmentation approach as above by increasing the size of the squares for sea lions and using the dice coefficient loss, it was able to provide satisfactory results in detecting the regions of interest but after running a regression it seems to overfit a lot, due to lack of time more experiments were not performed with this approach. Notebook for this experiment will be updated soon.

  3. FCN Regression: Thanks to @mrgloom we were able to get into top 100 using the fully connected net to perform regression on the whole image, an average of our two best-performing models give us our current position the leaderboard.

Requirements

  • Python 3.5 or above
  • Jupyter 4.3.1
  • Keras 1.2.2 & 2
  • Tensorflow 1.0 or above

License

This project is licensed under the MIT License - see the LICENSE.md file for details