segmentation_models
unet
segmentation_models | unet | |
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8 | 1 | |
4,611 | 4,445 | |
- | - | |
0.0 | 0.0 | |
4 months ago | 22 days ago | |
Python | Jupyter Notebook | |
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/.
unet
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U-net Returning Black Square As Prediction
Issue with the model architecture and what I’m trying to do with it? I’ve used the model from this GitHub project (https://github.com/zhixuhao/unet), as it seemed somewhat similar to what I was trying to do. I understand what is happening from layer to layer, but not the input/output parts. (I’ll put the code for it below)
What are some alternatives?
nnUNet
Human-Segmentation-PyTorch - Human segmentation models, training/inference code, and trained weights, implemented in PyTorch
efficientnet-lite-keras - Keras reimplementation of EfficientNet Lite.
ETCI-2021-Competition-on-Flood-Detection - Experiments on Flood Segmentation on Sentinel-1 SAR Imagery with Cyclical Pseudo Labeling and Noisy Student Training
efficientnet - Implementation of EfficientNet model. Keras and TensorFlow Keras.
cellpose - a generalist algorithm for cellular segmentation with human-in-the-loop capabilities
BlenderProc - A procedural Blender pipeline for photorealistic training image generation
drrmsan - DRRMSAN: Deep Residual Regularized Multi-Scale Attention Networks for segmentation of medical images. Machine Leaning 2 (DA330) Course Project, RKMVERI.
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.
Deep-Learning-In-Production - Build, train, deploy, scale and maintain deep learning models. Understand ML infrastructure and MLOps using hands-on examples.
rembg-greenscreen - Rembg Video Virtual Green Screen Edition
BCDU-Net - BCDU-Net : Medical Image Segmentation