Improve flood segmentation model

[melisandeteng]

Improve the current flood segmentation model , see #62

We report below a comparison of a first series of flood segmentation models using mIoU on our hand labeled dataset of 209 images from Keymakr.

DeepLab v2 with a ResNet-101 backbone (current model):

• trained on COCO-Stuff 164K dataset
• labels water-other, river, sand, sea were merged into the flood category mIoU ~ 79 %

DeepLab v2 with a ResNet-101 backbone + CRF postprocessing: mIoU ~ 80 % Extremely slow (> 10s / image)

Sazara et. al - pretrained VGG-16 + CRF postprocessing: fine tuned on 202 hand-labeled images mIoU ~ 77% slow : 7s/ image (and images had to be resized to have a max size of 600*600 pixels)

[melisandeteng]

Note that the masks for the flooded images will only need to be computed once. Next steps include :

• Fine tune Deeplabv2 model with the flood hand-labeled images
• Qualitative comparison of the models, on our Keymakr test set but also images from other sources
• Report of the failure cases
• Investigate other segmentation models - Deeplabv3, HRNet

[vict0rsch]

How's that going @melisandeteng ?