segmentation_models.pytorch
labelme
segmentation_models.pytorch | labelme | |
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14 | 6 | |
8,844 | 12,388 | |
- | 2.0% | |
4.1 | 8.7 | |
8 days ago | 5 days ago | |
Python | Python | |
MIT License | GNU General Public License v3.0 or later |
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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
labelme
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labelme VS anylabeling - a user suggested alternative
2 projects | 15 Apr 2023
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Use cases for PySide
Image, 3D, or data visualization applications using OpenCV and the SciPy ecosystem. The Graphics View Framework can display an image and let the user interact with it, and the Python ecosystem is very rich for image processing, data analysis, and visualization. For example, LabelMe for image labeling, PyQtGraph for scientific graphics, or custom QWidget integration in Maya.
- [D] What is a free tool for generating image segmentation masks?
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Convert Yolov3 annotation to labelme
Ref. - https://github.com/wkentaro/labelme/
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Mask RCNN Implementation for Image Segmentation | Tutorial
LabelMe is open-source tool for polygen image annotations inspired by MIT Label Me
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C++ trainable semantic segmentation models
Create your own dataset. Using labelme through "pip install" and label your images. Split the output json files and images into folders just like below:
What are some alternatives?
yolact - A simple, fully convolutional model for real-time instance segmentation.
labelme2coco - A lightweight package for converting your labelme annotations into COCO object detection format.
mmsegmentation - OpenMMLab Semantic Segmentation Toolbox and Benchmark.
Mask-RCNN-Implementation - Mask RCNN Implementation on Custom Data(Labelme)
face-parsing.PyTorch - Using modified BiSeNet for face parsing in PyTorch
Swin-Transformer-Semantic-Segmentation - This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" on Semantic Segmentation.
EfficientNet-PyTorch - A PyTorch implementation of EfficientNet and EfficientNetV2 (coming soon!)
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
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.
Mask_RCNN - Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow
pyannote-audio - Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding
sentinel2-cloud-detector - Sentinel Hub Cloud Detector for Sentinel-2 images in Python