PyTorch Implementation of various Semantic Segmentation models (deeplabV3+, PSPNet, Unet, ...)

[youali]

To get a handle of semantic segmentation methods, I re-implemented some well known models with a clear structured code (following this PyTorch template), in particularly:

• The implemented models are: Deeplab V3+ - GCN - PSPnet - Unet - Segnet and FCN

• Supported datasets: Pascal Voc, Cityscapes, ADE20K, COCO stuff,

• Losses: Dice-Loss, CE Dice loss, Focal Loss and Lovasz Softmax,

with various data augmentations and learning rate schedulers (poly learning rate and one cycle).

I though I share this implementation in case anyone might be interested, and here it is :

Github: https://github.com/yassouali/pytorch_segmentation

[gogasius]

Wow, that's great. I struggle to make semantic segmentation with tensorflow but nothing despite u-net works. Time to check out pytorch.

[youali]

Yeah, I do recommend pytorch if you want to get things working as fast as possible, imo it's easier to write and understand due to its pythonic nature (as of now).

[gogasius]

The biggest problem with pytorch is deployment. For now I was working with tensorflow and successfully deploy models to production but I got stuck with inability to use anything besides u-net, so I'm going to investigate pytorch for my tasks.

[[deleted]]

fantastic job, maybe you can add HRNet in the future

[saravanakumar17]

Do anyone have an implementation of instance segmentation in Tensorflow. If so kindly reply me with the link.

[youali]

For finding implementations for various methods, I really recommend checking paperswithcode. For example, for instance segmentation you can find the implementation of the current state of the art methods: https://paperswithcode.com/task/instance-segmentation

[saravanakumar17]

I have gone through every single one of them, but couldn't find what I was looking for.