[gcarvalho]

Thank you. I actually didn’t base it on fast.ai. It went on like so:

- I scraped from google images using queries like “ripe bananas”, “green bananas”, etc - Filtered out garbage images and labeled the remaining with the help of someone

- Trained the model (very straight forward with Keras). The code in the notebook is something like 30 lines I think.

- Using tf.js was what took me the longest. Using tensorflowjs_converter.save_model outputs an incompatible or corrupted file. Saving it first with keras and then using the tensorflowjs_converter CLI tool is what worked. The web-ui code is also available in the repo.

- Lastly. It is served from GitHub pages (not a problem since everything is static and runs client side), with a custom freenom domain (on a convenient .ml tld) and through cloudflare’s DNS which gives me SSL on a custom domain, caching and some very basic analytics.

Everything free tier. It only cost me time (around 5h I think).

Did I miss anything you wanted to know?