Semantic_Segmentation_Models

I am aiming to write different Semantic Segmentation models from scratch with different pretrained backbones.

1. DeepLabV3plus with SqueezeAndExcitation:

Paper(DeepLabV3plus): Encoder-Decoder with Atrous Separable Convolution

Paper(SqueezeAndExcitation): https://arxiv.org/pdf/1709.01507.pdf

Implementation:

1. DeepLabV3plus with EfficientNet as a backbone
2. DeepLabV3plus_SqueezeExcitation with EfficientNet as a backbone
3. DeepLabV3plus with ResNet as a backbone
4. DeepLabV3plus with DenseNet as a backbone
5. DeepLabV3plus with SqueezeNet as a backbone
6. DeepLabV3plus with VGG16 as a backbone
7. DeepLabV3plus with ResNext101 as a backbone. We need to clone qubevl's classification_models repository (I have used the pre-trained model from this repository).

2. Global Convolutional Network (GCN):

Paper link: GCN

Implementation:

1. GCN with ResNet50_v2 as a backbone
2. GCN with EfficientNetB0 as a backbone

3. PSPNet:

Paper link: Pyramid Scene Parsing Network

Implementation:

1. PSPNet with ResNet50 as a backbone

4. DeepLabV3Plus_PSPNet_SE:

This model is to try whether Deeplabv3p model with an added PSPNet module for deeper semantic feature extraction and Squeeze&Excitation modules to add channel-wise attention. I will be sharing results of this if this works out well.

5. Unet:

Paper link: U-Net: Convolutional Networks for Biomedical Image Segmentation

Implementation:

1. Unet with MobileNetV2 as a backbone
2. Unet with EfficientNet as a backbone Coming Soon...
3. Unet with ResNet50 as a backbone Coming Soon...

Note: We can directly use segmentation_models package for Unet (Github: http://github.com/qubvel/segmentation_models).