nnUNet
segmentation_models.pytorch
nnUNet | segmentation_models.pytorch | |
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11 | 14 | |
5,045 | 8,844 | |
3.6% | - | |
9.2 | 4.1 | |
3 days ago | 2 days ago | |
Python | Python | |
Apache License 2.0 | MIT License |
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nnUNet
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Where to even begin to extract yellow spots from these pictures?
Now just run your images and masks in nnUNet to see if it works. https://github.com/MIC-DKFZ/nnUNet
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[D] Improvements/alternatives to U-net for medical images segmentation?
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
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Seeking ideas for Road Damage Detection
I used the nnUnet repo and their guide on using it on your own data
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Model conversion from Pytorch to Tf using Onnx.
Hi all, Does anyone have experience with converting a nnUNet pytorch models into Tf using Onnx?
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[D] Best practices for training medical imaging models
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.
- Image segmentation: huggingface segformers vs detectronv2
- How to get started with 3D computer vision?
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3D rcnn
Not a "detection" but a "segmenting" CNN: https://github.com/MIC-DKFZ/nnunet
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Underwhelming performance of Tesla V100 and A100 in vast.ai, why?
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.
- [D] Which approach is recommended for Brain 🧠Tumor Segmentation on MRI scans (DICOM Files)?
segmentation_models.pytorch
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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
What are some alternatives?
segmentation_models - Segmentation models with pretrained backbones. Keras and TensorFlow Keras.
yolact - A simple, fully convolutional model for real-time instance segmentation.
3d-multi-resolution-rcnn - Official PyTorch implementaiton of the paper "3D Instance Segmentation Framework for Cerebral Microbleeds using 3D Multi-Resolution R-CNN."
mmsegmentation - OpenMMLab Semantic Segmentation Toolbox and Benchmark.
medicaldetectiontoolkit - The Medical Detection Toolkit contains 2D + 3D implementations of prevalent object detectors such as Mask R-CNN, Retina Net, Retina U-Net, as well as a training and inference framework focused on dealing with medical images.
face-parsing.PyTorch - Using modified BiSeNet for face parsing in PyTorch
mmpose - OpenMMLab Pose Estimation Toolbox and Benchmark.
EfficientNet-PyTorch - A PyTorch implementation of EfficientNet and EfficientNetV2 (coming soon!)
albumentations - Fast image augmentation library and an easy-to-use wrapper around other libraries. Documentation: https://albumentations.ai/docs/ Paper about the library: https://www.mdpi.com/2078-2489/11/2/125
SegmentationCpp - A c++ trainable semantic segmentation library based on libtorch (pytorch c++). Backbone: VGG, ResNet, ResNext. Architecture: FPN, U-Net, PAN, LinkNet, PSPNet, DeepLab-V3, DeepLab-V3+ by now.
s2ds
pyannote-audio - Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding