PaddleSeg
Easy-to-use image segmentation library with awesome pre-trained model zoo, supporting wide-range of practical tasks in Semantic Segmentation, Interactive Segmentation, Panoptic Segmentation, Image Matting, 3D Segmentation, etc. (by PaddlePaddle)
segmentation_models.pytorch
Segmentation models with pretrained backbones. PyTorch. (by qubvel)
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PaddleSeg | segmentation_models.pytorch | |
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17 | 14 | |
8,253 | 8,800 | |
2.2% | - | |
7.4 | 2.8 | |
12 days ago | 6 days ago | |
Python | Python | |
Apache License 2.0 | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
PaddleSeg
Posts with mentions or reviews of PaddleSeg.
We have used some of these posts to build our list of alternatives
and similar projects.
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[Medical Segmentation] The all-in-one 3D medical image segmentation toolkit. From data annotation to model deployment, you are welcome to try it all!
EISeg-Med3D: https://github.com/PaddlePaddle/PaddleSeg/blob/develop/EISeg/med3d/README_en.md
Thank you for your compliment. You are very welcome to try it. If you have any problem, please don't hesitate to put an issue here: https://github.com/PaddlePaddle/PaddleSeg/issues.
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[N][R] SOTA Real-Time Semantic Segmentation Model Implemented by Python
Source code implemented by python and models: https://github.com/PaddlePaddle/PaddleSeg
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[P] New Features of Image Segmentation Project PaddleSeg (6k stars)
Github: https://github.com/PaddlePaddle/PaddleSeg
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[Trending Summary] neurips @ 2022-11-28 09:55
https://github.com/PaddlePaddle/PaddleSeg/blob/release/2.6/EISeg/README_EN.md Open-source, easy-to-use and powerful. Provides specialized models for better performance.
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[P] Awesome Interactive Segmentation Improves 10X Segmentation Annotation Efficiency (6k star)
Found relevant code at https://github.com/PaddlePaddle/PaddleSeg + all code implementations here
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Open-source SOTA Solution for Portrait and Human Segmentation (5.7k stars)
Github: https://github.com/PaddlePaddle/PaddleSeg/tree/release/2.6/contrib/PP-HumanSeg
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[Medical Segmentation] The all-in-one 3D medical image segmentation python toolkit. From data annotation to model deployment, you are welcome to try it all!
you can put forward an issue here: https://github.com/PaddlePaddle/PaddleSeg/issues/new/choose
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[R] RTFormer <NeurIPS 2022> : Real-Time Semantic Segmentation with Transformer
Official code is available at: https://github.com/PaddlePaddle/PaddleSeg
segmentation_models.pytorch
Posts with mentions or reviews of segmentation_models.pytorch.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-12-09.
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Instance segmentation of small objects in grainy drone imagery
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
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[D] Improvements/alternatives to U-net for medical images segmentation?
SMP offers a wide variety of segmentation models with the option to use pre-trained weights.
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Improvements/alternatives to U-net for medical images segmentation?
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
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Medical Image Segmentation Human Retina
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
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Automatic generation of image-segmentation mask pairs with StableDiffusion
Sounds like a good semantic segmentation problem, I like this repo: https://github.com/qubvel/segmentation_models.pytorch
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Dice Score not decreasing when doing semantic segmentation
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?
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Good Brain Tumor segmentation model !?
I know there is a decent one in segmentation models python (MA-Net: A Multi-Scale Attention Network for Liver and Tumor Segmentation)
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Advice needed
You could also use qubvel's segmentation models if you would like to explore semantic segmentation.
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[D][R] Is there a standard architecture for U-Nets, pixel-to-pixel models, VAEs, and the like?
Check out segmentation models pytorch, really easy to use, has a great interface.
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Pytorch GPU Memory Leak Problem: Cuda Out of Memory Error !!
Have you tried another implementation? For example: qubvel/segmentation_models.pytorch