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10 Lessons Learned From Participating in Google AI Challenge

Disclaimers: I will present only a portion of the code I wrote for this competition, my teammates are absolutely not responsible for my awful and buggy code.

It contains 50 millions images with details for each image: in which category the image falls, a unique identifier (key_id), in which country the drawing come from (country) and the strokes points (drawing) to reproduce the drawing.

But with that solution, with such a big dataset, I faced another challenge: my Linux filesystem wasn’t configured to support this huge number of inodes (50 millions files added to the filesystem thus 50 millions new inodes created) and ended up with a filesystem full without using all the GB available.

My initial intention was to implement parallel workers accessing the database in read-only mode by providing one SQLite file object per worker, but before going that way I first tried using a global lock on a single SQLite object and it surprisingly gave immediate decent results (GPU used at 98%), that’s why I did not even bother to improve it.

To do so I get inspired from Beluga’s kernel, which serve as a baseline for many competitors: But I noticed some issues with this piece of code: Following improvements were done: Note: I did not encode velocity or time provided, it would probably add more information that the CNN could use.

I made this decision based on the fact the dataset with its 50 millions images is pretty huge and imagenet images (real world pictures) are pretty far from a 10-seconds drawn sketch.

The model presented is a stock torchvision model, but with a custom head similar to the original, but in which I replaced the final pooling layer with an adaptive counterpart (AdaptiveAvgPool2d) to handle gracefully images of different resolutions.

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