ML project revolving around guessing location by looking at street view image.
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Download as many street view images as possible, trim and compress
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Create classes using S2 library
Used similar partition technique as this paper: https://research.google.com/pubs/archive/45488.pdf
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Split dataset into classes, try to get balanced datasets
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Download a lot more of street view images for small classes, islands, cities and alike (not everything is on google street view as you can see):
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Train ResNet-50 with pretrained weights (changed first and last layer to accomodate input and output) using automated script that deploy code and train on https://runpod.io
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Run, test, repeat training. Add well-known augmentations, custom augmentation (PanoramaShifting), more trimming, lower resolution etc.
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Used GradCamPlusPlus (check_net.py) to see what ResNet is looking at to make its decisions
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My best attempt (~geoguessr Master level) used "voting algorithm" on images from multiple years of street view
- Add multiple heads to guess country/continent and location