segmentation_models
segmentation_models.pytorch
segmentation_models | segmentation_models.pytorch | |
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8 | 14 | |
4,611 | 8,800 | |
- | - | |
0.0 | 2.8 | |
4 months ago | 8 days ago | |
Python | Python | |
MIT License | MIT License |
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segmentation_models
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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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segmentation-models No module Error
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'
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recommendations for semantic segmentation of lowish volumes of biomedical images
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.
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Can we use autoencoders to change an existing image instead of create one from scratch?
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.
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Anyone implemented latest image segmentation models/tuning from cvpr 2021?
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?
- Semantic Segmentation
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Any way to speed up inference prepare operations on host (CPU)?
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.
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D Simple Questions Thread December 20 2020
I'm trying to train image segmentation model with transfer learning using https://github.com/qubvel/segmentation_models/.
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?
nnUNet
yolact - A simple, fully convolutional model for real-time instance segmentation.
efficientnet-lite-keras - Keras reimplementation of EfficientNet Lite.
mmsegmentation - OpenMMLab Semantic Segmentation Toolbox and Benchmark.
efficientnet - Implementation of EfficientNet model. Keras and TensorFlow Keras.
face-parsing.PyTorch - Using modified BiSeNet for face parsing in PyTorch
BlenderProc - A procedural Blender pipeline for photorealistic training image generation
EfficientNet-PyTorch - A PyTorch implementation of EfficientNet and EfficientNetV2 (coming soon!)
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.
rembg-greenscreen - Rembg Video Virtual Green Screen Edition
pyannote-audio - Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding