Vanilla U-Net based approach for retinal image segmentation
Even though this was not intended in paper but BatchNorm layers added before every ReLu layer . In new version because of that converging speeded up.
It is well known that U-Net is network which mainly used in Segmentation tasks. In this application i implemented network from scratch using U-Net paper U-Net paper
RITE dataset relatively small that is why data augmentation applied (5 degree rotation for every single image)(256x256x3)
Here is simplified network architecture
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This repository doesnt includes any training dataset if you want to get some training material check out RITE Dataset
- Requirements :
- OpenCV
- PyTorch
- NumPy
- TorchVision
Here is a result from testset:
Learning Curve:
