unet
BCDU-Net
| unet | BCDU-Net | |
|---|---|---|
| 1 | 1 | |
| 4,445 | 676 | |
| - | - | |
| 0.0 | 0.0 | |
| 27 days ago | over 1 year ago | |
| Jupyter Notebook | Python | |
| MIT License | - |
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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)
BCDU-Net
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[D] Extensions to U-nets
I compared a U-net, BCDU-net, and a U2-net for glacier semantic segmentation which is a pretty easy task. I don't still have the exact numbers, but U2-net was the best. I've also used a U2-net to map geologic structures which is a lot harder and the U2-net did well there too.
What are some alternatives?
Human-Segmentation-PyTorch - Human segmentation models, training/inference code, and trained weights, implemented in PyTorch
Pytorch-UNet - PyTorch implementation of the U-Net for image semantic segmentation with high quality images
segmentation_models - Segmentation models with pretrained backbones. Keras and TensorFlow Keras.
U-2-Net - The code for our newly accepted paper in Pattern Recognition 2020: "U^2-Net: Going Deeper with Nested U-Structure for Salient Object Detection."
ETCI-2021-Competition-on-Flood-Detection - Experiments on Flood Segmentation on Sentinel-1 SAR Imagery with Cyclical Pseudo Labeling and Noisy Student Training
GlacierSemanticSegmentation - Identify glaciers in satellite images with a U^2-Net
cellpose - a generalist algorithm for cellular segmentation with human-in-the-loop capabilities
perin - PERIN is Permutation-Invariant Semantic Parser developed for MRP 2020
drrmsan - DRRMSAN: Deep Residual Regularized Multi-Scale Attention Networks for segmentation of medical images. Machine Leaning 2 (DA330) Course Project, RKMVERI.
UNetPlusPlus - [IEEE TMI] Official Implementation for UNet++
Deep-Learning-In-Production - Build, train, deploy, scale and maintain deep learning models. Understand ML infrastructure and MLOps using hands-on examples.
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.