I trained a model that takes a picture of a road accident, and tells us if its a car crash or a bike crash. The accuracy is about 93% with a resnet-34 model, before any data cleaning.
Most of the misclassified images had both, a car and a bike, involved in a crash.
After some data-cleaning (removing duplicate images, deleting unrelated images), I was able to improve the model performance to about 95%., where I used various learning rates to see what works best.
Here’s a reference notebook, if someone is interested: https://github.com/deven299/Crash
Thanks to fastai team and the community