deep learning

Extending Keras ImageDataGenerator to handle multilable classification tasks

I stumbled up on this problem recently, working on one of the kaggle competitions which featured a multi label and very unbalanced satellite image dataset.

Let’s talk a moment about a neat Keras feature which is keras.preprocessing.image.ImageDataGenerator as you can see from the documentation its main purpose is to augment and generate new images from your dataset. This is a common tactic to fight small datasets and overfitting.
By default ImageDataGenerator expects our data to be structured in a very specific way, this is each class should have its own directory and every image inside this directory belongs to the class specified by the name of this directory.
We can realize that this is very limiting and usage of this API directly will not work for Multi-label problems.

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Posted by jakub.cieslik, 0 comments

Developing a deep learning edge detector to solve a “toy” problem

TLDR

In an attempt to solve a children-shape-puzzle-game a deep learning based edge detector was developed. It’s based on the U-Net image segmentation architecture and trained on the BSDS 500 dataset.

fig1. children shape puzzle problem we try to solve

I just want the code:
github.com/i008/deepedge

I just want to try it on my own images:

Go to the edge_notebook.ipynb

I just want to see how it works:
check results here

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Posted by jakub.cieslik, 0 comments