Ajith,
I had thought about the same questions, here are my findings:
TLDR; winning solution uses VGG16 and manually crops certain parts of the image for the CNNs to focus on.
- Statefarm dataset is relatively small, and reusing the convolution layers from VGG would ideally help the model avoid overfitting.
Visualizing what VGG+keras is looking for:
excellent visuals (zeiler / fergus style):
- Its quite clear that certain parts of the image are more important than others, and the leading results certainly had many creative methods to think about this.
This discussion showed a competitor’s method to display with a very cool heatmap of what the CNNs were focusing on:
1st place solution uses VGG16 and combines 3 images; a cropped image around the head, a cropped image around where the hand may be, and the original image itself.
10th place solution involved finding the area around the drivers body, and cropping the image to around that.
Statefarm was a super cool competition to work on, and I feel like it was a quantum leap from dogs-and-cats competition in terms of deep learning understanding. Hope my findings were helpful, the fact that the winning solution used something more elegant in the spirit of deep learning was certainly very encouraging!
best,
Jerry