segmentation_models VS nnUNet

Also, I’d suggest considering switching to the segmentation-models library - it provides U-Net models with a variety of pretrained backbones of as encoders. The author also put out a PyTorch version. https://github.com/qubvel/segmentation_models.pytorch https://github.com/qubvel/segmentation_models

I used segmentation-models (https://github.com/qubvel/segmentation_models) to create a deeplabv3+ model. I havent used it in the last 2 months and now i comeback to the same code and cant use it. Getting ModuleNotFoundError: No module named 'segmentation_models_pytorch.deeplabv3'

I'm building some semantic segmentation models off of low-moderate volumes of biomedical images (~500 - 1k images). So far I've done some hyperparameter sweeping (learning rate, transfer learning, architectures, dropout layers) using the Segmentation Models package from qubvel https://github.com/qubvel/segmentation_models but I'm only seeing moderate performance and minimal differences between tested parameters.

So, image segmentation (especially for satellite images) is a known problem. Search for semantic segmentation and unet (a model used for semantic segmentation). Also, if you use tensorflow there is this library which I found useful segmentation models.

I am doing an image segmentation project using https://github.com/qubvel/segmentation_models as the baseline. I was wondering if any of you have tried the latest segmentation models from cvpr papers. If yes, which ones you found to be interesting or actually improve miou. And how difficult/easy it is to implement those?

That is just U-net from this repo, anything aside is slicing images to fit into window and predict call. I measure time of predict() and it is the same as profiler numbers, so definitely my other operations are beyond profiler. C API code is just creating tensors and calling TF_SessionRun plus slice operations with opencv. Can't post code, sorry.

I'm trying to train image segmentation model with transfer learning using https://github.com/qubvel/segmentation_models/.

Now just run your images and masks in nnUNet to see if it works. https://github.com/MIC-DKFZ/nnUNet

I would also try the nnU-Net which should give state-of-the-art performance, and so will give you a good idea of what's possible with the dataset that you have: https://github.com/MIC-DKFZ/nnUNet

I used the nnUnet repo and their guide on using it on your own data

Hi all, Does anyone have experience with converting a nnUNet pytorch models into Tf using Onnx?

If you want my honest opinion, you should probably always use nnUnet as a baseline for any 3d segmentation tasks - https://github.com/MIC-DKFZ/nnUNet.

Not a "detection" but a "segmenting" CNN: https://github.com/MIC-DKFZ/nnunet

I ran some model fitting performance benchmarks on a 3D (medical) image segmentation UNET on various cloud providers and I am flabbergasted by the very low performance of V100 and A100 machines on vast.ai.