One of the first skills acquired in the latest version of the fast.ai course on deep learning is how to create a production version of an image classifier that runs as a web application. I decided to test this out on a set of images of road bikes, TT bikes and mountain bikes. To try it out, click on the image above or go to this website https://bike-identifier.onrender.com/ and select an image from your device. If you are using a phone, you can try taking photos of different bikes, then click on Analyse to see if they are correctly identified. Side-on images work best.
How does it work?
The first task was to collect some sample images for the three classes of bicycles I had chosen: road, TT and MTB. It turns out that there is a neat way to obtain the list of urls for a Google image search, by running some javascript in the console. I downloaded 200 images for each type of bike and removed any that could not be opened. This relatively small data set allowed me to do all the machine learning using the CPU on my MacBook Pro in less than an hour.
The fast.ai library provides a range of convenient ways to access images for the purpose of training a neural network. In this instance, I used the default option of applying transfer learning to a pre-trained ResNet34 model, scaling the images to 224 pixel squares, with data augmentation. After doing some initial training, it was useful to look at the images that had been misclassified, as many of these were incorrect images of motorbikes or cartoons or bike frames without wheels or TT bars. Taking advantage of a useful fast.ai widget, I removed unhelpful training images and trained the model further.
The confusion matrix showed that final version of my model was running at about 90% accuracy on the validation set, which was hardly world-beating, but not too bad. The main problem was a tendency to mistake certain road bikes for TT bikes. This was understandable, given the tendency for road bikes to become more aero, though it was disappointing when drop handlebars were clearly visible.
The next step was to make my trained network available as a web application. First I exported the models parameter settings to Dropbox. Then I forked a fast.ai repository into my GitHub account and edited the files to link to my Dropbox, switching the documentation appropriately for bicycle identification. In the final step, I set up a free account on Render to host a web service linked to my GitHub repository. This automatically updates for any changes pushed to the repository.
Amazingly, it all works!
References
My GitHub repository, include Jupyter notebook
Science4Performance 
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