mmsegmentation
Swin-Transformer-Semantic-Segmentation
| mmsegmentation | Swin-Transformer-Semantic-Segmentation | |
|---|---|---|
| 7 | 1 | |
| 7,414 | 1,081 | |
| 1.8% | 0.0% | |
| 8.2 | 0.0 | |
| 7 days ago | over 1 year ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
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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.
Swin-Transformer-Semantic-Segmentation
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[P] Code and pretrained models for Swin Transformer are released (SOTA models on COCO and ADE20K)
Semantic segmentation on ADE20K: https://github.com/SwinTransformer/Swin-Transformer-Semantic-Segmentation
What are some alternatives?
Pytorch-UNet - PyTorch implementation of the U-Net for image semantic segmentation with high quality images
Swin-Transformer-Object-Detection - This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" on Object Detection and Instance Segmentation.
segmentation_models.pytorch - Segmentation models with pretrained backbones. PyTorch.
labelme - Image Polygonal Annotation with Python (polygon, rectangle, circle, line, point and image-level flag annotation).
Mask_RCNN - Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow
Swin-Transformer-Serve - Deploy Swin Transformer using TorchServe
face-parsing.PyTorch - Using modified BiSeNet for face parsing in PyTorch
Video-Swin-Transformer - This is an official implementation for "Video Swin Transformers".
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.
PaddleClas - A treasure chest for visual classification and recognition powered by PaddlePaddle
labelme2coco - A lightweight package for converting your labelme annotations into COCO object detection format.
SeMask-Segmentation - [NIVT Workshop @ ICCV 2023] SeMask: Semantically Masked Transformers for Semantic Segmentation