There’s a jupyter notebook available here that contains all the code to build the model. To generate some predictions, we’ll first load the weights of the best model using model.load_weights(‘weights/best_weights.hdf5’) and then use our model’spredict method on some of the images in our validation set: Here’s a sampling of some of the results: While this isn’t good enough for a production use-case yet, the network has learned something about foreground vs background and chair vs non-chair. An interesting part of their innovation is a custom rotating photo studio that automatically captures and processes 16 standard images … A script of model definition. Normally I wouldn’t expect to be able to do much with such limited data (the Carvana challenge provided thousands of examples) but let’s see how far we can get with it. This tells us the networks expects a batch of 256x256 3 channel images as an input, and will output a batch of 256x256 single channel masks. The validation fold won’t be used for training, we’ll only use the validation fold to check the performance of the model. Essentially, we want to go from this: We’re going to use a few tools to make our lives easier. Our goal is going to be to repurpose this solution to solve our furniture segmentation problem.

If we rotate or flip the image, we have to perform the same operation on the mask so that the mask stays aligned with the original image. This reduced inference time by 40% in my tests. For example, a chair rotated by 5 degrees is still a chair, so the network should be able to identify that correctly. If nothing happens, download Xcode and try again. brine — (I’m one of the developers) A dataset manager to make it easy to share and manipulate image datasets.
Kaggle-Carvana-3rd-place-solution. Copy over the model directory from the Kaggle-Carvana-Image-Masking-Challenge github repo so we have it available to us. Use Git or checkout with SVN using the web URL. Customized implementation of the U-Net in PyTorch for Kaggle's Carvana Image Masking Challenge from high definition images..

If you were to print image.shape at this point, you’d see that it’s 256x256x3, exactly what our network needs.
model.py / model_pytorch.py. Network detail is shown on below figure. As you’ll see, U-Net works surprisingly well even when you don’t have a large dataset. We won’t actually need to use Keras directly in this guide, but if you peek under the hood Keras is what you’ll see.

The next two lines use numpy slicing to ensure that we only have 3 channel images — if there’s a fourth alpha channel, we’re ignoring it.

Before we start training the network, we also need to set some of the samples aside to be used for validation.

Keras will train the model, running through the dataset multiple times (though each run will be slightly different because of data augmentation and shuffling) and output our losses and DICE score for our train and validation set. At some point, we’ll stop either because of our EarlyStopping callback or because we hit 100 epochs. The first element of the tuple is referring to the mini-batch size, so we can ignore that for now.

I’ll step through the important parts and explain what it’s doing.

These are: keras — An awesome library for building neural networks. GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download Xcode and try again. Personally I find this quite amazing given that the network was trained on only 77 images without any pre-training. And that’s it! Convert training masks to .png format.

this github repo — The Carvana Image Masking Challenge was a Kaggle competition posing a similar problem: segmenting out cars from their background.

Next, we convert the mask to grayscale using cv2 (python’s bindings to OpenCV), so we now have a single channel mask as our network expects.

I’m not an expert in machine learning myself, so my hope is that this post will be useful to other non-experts looking to use this powerful new tool. Learn more.

Our masks need to match this shape as well. That’s it! In this post, I’ll walk through how we can use the current state-of-the-art in deep learning to try and solve this problem. Now we’re ready to train the model.

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