labelme2coco
mmsegmentation
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| labelme2coco | mmsegmentation | |
|---|---|---|
| 1 | 7 | |
| 246 | 7,380 | |
| - | 3.9% | |
| 3.8 | 8.6 | |
| 3 days ago | 9 days ago | |
| Python | Python | |
| GNU General Public License v3.0 only | Apache License 2.0 |
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labelme2coco
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What's A Simple Custom Segmentation Pipeline?
I would also suggest labelme, it's pretty easy to use. Just type "labelme" in the shell after pip installing and you will see the GUI. There are tools to convert to coco format (like https://github.com/fcakyon/labelme2coco) if needed, for instance for Detectron2.
mmsegmentation
- [D] The MMSegmentation library from OpenMMLab appears to return the wrong results when computing basic image segmentation metrics such as the Jaccard index (IoU - intersection-over-union). It appears to compute recall (sensitivity) instead of IoU, which artificially inflates the performance metrics.
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Is there any ML model out there for room surfaces detection? (ceiling, floor, windows)
Segmentation models trained on datasets like ADE20k could probably be used for that, because it has separate classes for these things iirc. https://github.com/open-mmlab/mmsegmentation should have suitable pretrained models available.
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MMDeploy: Deploy All the Algorithms of OpenMMLab
MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark.
- Mmsegmentation - Openmmlab semantic segmentation toolbox and benchmark.
- Mmsegmentation – Openmmlab semantic segmentation toolbox and benchmark
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Semantic Segmentation models
This repo is amazing: https://github.com/open-mmlab/mmsegmentation
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What's A Simple Custom Segmentation Pipeline?
Mmsegmentation would be a good place to start for basic segmentation. They have lots of recent methods and pretained models you could fine-tune from. They also support quite a few datasets including VOC. There is a custom dataset format which looks straightforward to create.
What are some alternatives?
labelme - Image Polygonal Annotation with Python (polygon, rectangle, circle, line, point and image-level flag annotation).
Pytorch-UNet - PyTorch implementation of the U-Net for image semantic segmentation with high quality images
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
Swin-Transformer-Semantic-Segmentation - This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" on Semantic Segmentation.
bpycv - Computer vision utils for Blender (generate instance annoatation, depth and 6D pose by one line code)
segmentation_models.pytorch - Segmentation models with pretrained backbones. PyTorch.
coco-viewer - Minimalistic COCO Dataset Viewer in Tkinter
Mask_RCNN - Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow
autogluon - AutoGluon: Fast and Accurate ML in 3 Lines of Code
face-parsing.PyTorch - Using modified BiSeNet for face parsing in PyTorch
mask-rcnn - Mask-RCNN training and prediction in MATLAB for Instance Segmentation
PaddleSeg - Easy-to-use image segmentation library with awesome pre-trained model zoo, supporting wide-range of practical tasks in Semantic Segmentation, Interactive Segmentation, Panoptic Segmentation, Image Matting, 3D Segmentation, etc.