Playing with open-images
Did a benchmark of multi-class classification models and approaches useful in general with multi-tier classificators.
The basic idea is - follow the graph structure of class dependencies - train a good multi-class classifier => train coarse semseg models for each big cluster.
What worked
- Using SOTA classifiers from imagenet
- Pre-training with frozen encoder (otherwise the model performes worse)
- Best performing architecture so far - ResNet152 (a couple of others to try as well)
- Different resolutions => binarise them => divide into 3 major clusters (2:1,1:2,1:1)
- Using adaptive pooling for different aspect ratio clusters
What did not work or did not significantly improve results
- Oversampling
- Using modest or minor augs (10% or 25% of images augmented)
What did not work
- Using 1xN + Nx1 convolutions instead of pooling - too heavy
- Using some minimal avg. pooling (like 16x16), then using different 1xN + Nx1 convolutions for different clusters - performed mostly worse than just adaptive pooling
Yet to try
- Focal loss
- Oversampling + augs
#deep_learning
Did a benchmark of multi-class classification models and approaches useful in general with multi-tier classificators.
The basic idea is - follow the graph structure of class dependencies - train a good multi-class classifier => train coarse semseg models for each big cluster.
What worked
- Using SOTA classifiers from imagenet
- Pre-training with frozen encoder (otherwise the model performes worse)
- Best performing architecture so far - ResNet152 (a couple of others to try as well)
- Different resolutions => binarise them => divide into 3 major clusters (2:1,1:2,1:1)
- Using adaptive pooling for different aspect ratio clusters
What did not work or did not significantly improve results
- Oversampling
- Using modest or minor augs (10% or 25% of images augmented)
What did not work
- Using 1xN + Nx1 convolutions instead of pooling - too heavy
- Using some minimal avg. pooling (like 16x16), then using different 1xN + Nx1 convolutions for different clusters - performed mostly worse than just adaptive pooling
Yet to try
- Focal loss
- Oversampling + augs
#deep_learning