Segmentation_models.pytorch Alternatives

segmentation_models.pytorch reviews and mentions

• Instance segmentation of small objects in grainy drone imagery
  8 projects | /r/computervision | 9 Dec 2023

  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

• [D] Improvements/alternatives to U-net for medical images segmentation?
  2 projects | /r/MachineLearning | 29 Mar 2023

  SMP offers a wide variety of segmentation models with the option to use pre-trained weights.

• Improvements/alternatives to U-net for medical images segmentation?
  1 project | /r/deeplearning | 29 Mar 2023

  SMP has a lot of different choices for architecture other than unet, and a ton of different encoders. I like deeplabv3+/unet with regnety encoder, works well for most things https://github.com/qubvel/segmentation_models.pytorch

• Medical Image Segmentation Human Retina
  1 project | /r/computervision | 19 Jan 2023

  This basic example from segmentation models PyTorch repo would be good tutorial to start with. The library is very good, I like the unet, fpn and deeplabv3+ architectures with regnety as encoder https://github.com/qubvel/segmentation_models.pytorch/blob/master/examples/binary_segmentation_intro.ipynb

• Automatic generation of image-segmentation mask pairs with StableDiffusion
  1 project | /r/computervision | 9 Jan 2023

  Sounds like a good semantic segmentation problem, I like this repo: https://github.com/qubvel/segmentation_models.pytorch

• Dice Score not decreasing when doing semantic segmentation
  1 project | /r/learnmachinelearning | 17 Apr 2022

  When i pass the CT-Scans and the masks to the Loss Function, which is the Jaccard-Loss from the segmentation_models.pytorch library, the value does not decrease but stay in the range of 1.0-0.9 over 50 epochs training on only one batch of 32 images. As far as I have understood, my network should overfit and the loss should decrease since I am only training on one batch of a small amount of images. However this does not happen. I also tried more batches with all the data over 100 epochs, but the loss does not decrease either obviously. Does anyone have an idea what I might have done wrong? Do I have to change anything when passing the masks to my loss function?

• Good Brain Tumor segmentation model !?
  1 project | /r/computervision | 30 Mar 2022

  I know there is a decent one in segmentation models python (MA-Net: A Multi-Scale Attention Network for Liver and Tumor Segmentation)

• Advice needed
  3 projects | /r/computervision | 2 Oct 2021

  You could also use qubvel's segmentation models if you would like to explore semantic segmentation.

• [D][R] Is there a standard architecture for U-Nets, pixel-to-pixel models, VAEs, and the like?
  1 project | /r/MachineLearning | 27 Sep 2021

  Check out segmentation models pytorch, really easy to use, has a great interface.

• Pytorch GPU Memory Leak Problem: Cuda Out of Memory Error !!
  1 project | /r/pytorch | 15 Sep 2021

  Have you tried another implementation? For example: qubvel/segmentation_models.pytorch

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