Modelling improvements #3
Description
- Go through notebooks and apply documented thoughts
- Revisit learning rate finder experiments:
fit_one_cycle()orfit() - Incorporate object detection (e.g., auto-crop and train without too much of the background)
- Incorporate text descriptions (as input/output)
- Revisit and collect notes from more advanced fast.ai notebooks
- Look into https://github.com/libffcv/ffcv
- Look into a smaller model without losing accuracy: https://wandb.ai/fastai/fine_tune_timm/reports/Finding-the-New-ResNet18--VmlldzoyMDI0MjU3