- Do some exploratory analysis, so you can view any image and it's bounding boxes
- Setup fastai datablock api to read train/validation sets.
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Setup how to transform output of CNN into output for submission -
See how a simple RNN performs. Don't need online learning, so accuracy over speed. -
See where I am, rinse and repeat? Study more FastAI courses, see if I can improve this score - Build an initial character recognition network from scratch, on subset
- Examine hooks network from scratch, see what is was looking for?
- Try to build a character recognition network for all chars
- Build object recognition from subset
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With small object recognition model, classified boxes, then feed them into small character recognition model - Repeat for large model
The strikethroughs are inteded to show what I had PLANNED on doing, but as my learning evolved, so did my understanding of what I wanted to do next
exploratory_analysis takes a peek at the initial data, and renders a sample
prepare_character_set contains code for creating the dataset and exploring a 2 class cnn.
character_recognizer has a 10 class CNN
character_recognzier_v2 has the full CNN for all Kuzushiji characters
object_recognizer_v1 contains initial attempts to do full character object detection on a page