###U-net
[x] Implement ResNet34 encoded U-Net
[x] Use pretrain weights
[x] Compare pretrained encoder with full initialized
[X] Implement deeper encoders (ResNet50,101,152)[x] Compare transposed convolutions with bilinear upsampling
[x] No pooling in center block: 4 upsamplings
[x] Add pooling in center block: 5 upsamplings
[x] Try spatial dropout
[ ] Replace part of the decoder with dilated convs[x] Implement hyper columns on decoder
[ ] Increment hyper columns using adptation convolutions[x] Implement sSE and cSC on decoder
[x] Implement sSE and cSC on encoder
[x] Use cSE block as input to image depth[x] Implement PPM on U-Net's center block
[ ] Implement OC-ASP Module
###FPNet [x] Implement FPNet
[ ] Add SE Blocks
###RefineNet [x] Implement RefineNet
[x] Add SE blocks
[ ] Implement dense connections in RefineBlocks
[ ] Use PPM or ASSP instead of ChainPoolingModule
[ ] Use hypercolumns
###DeepLabv3 [ ] Implement DeepLabv3
[ ] Add SE blocks
[ ] Implement OC-ASP Module
[x] Implement learnig rate reduction on Plateau
[x] Implement learnig rate reduction on Milestones[x] Implement cosine annealing with warm restart
[x] Verify if getting predictions for the M last restarts can produce a good ensemble[x] Implement Binary Cross Entropy
[x] Implement Sørensen–Dice loss (IoU)
[x] Implement Hybrid BCE+Dice
[x] Implement Lovász-hinge loss
[x] Implement Hybrid BCE+Lovás loss
[ ] Add Regional Loss: Adaptive Affinity Field[x] Implement 5Fold cross-validation
[x] Implement stratified cross-validation by mask coverage
[x] Ensemble predictions (average) on 5 folds[x] Implement basic augmentations (flips, shift, scale, crop, elastic, rotate, shear, gamma, brightness)
[x] Use additional dataset made of masks with small salt coverage
[ ] Test other types of augmentations[x] Implement simple flip TTA
[ ] Implement other types of TTA