mmgeneration
mmsegmentation
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| mmgeneration | mmsegmentation | |
|---|---|---|
| 2 | 7 | |
| 1,796 | 7,380 | |
| 2.9% | 3.9% | |
| 2.4 | 8.6 | |
| 8 months ago | 11 days ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
mmgeneration
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MMDeploy: Deploy All the Algorithms of OpenMMLab
MMGeneration: OpenMMLab image and video generative models toolbox.
- Defect Detection using RPI
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?
anomalib - An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.
Pytorch-UNet - PyTorch implementation of the U-Net for image semantic segmentation with high quality images
mmrotate - OpenMMLab Rotated Object Detection Toolbox and Benchmark
Swin-Transformer-Semantic-Segmentation - This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" on Semantic Segmentation.
mim - MIM Installs OpenMMLab Packages
segmentation_models.pytorch - Segmentation models with pretrained backbones. PyTorch.
pytorch-CycleGAN-and-pix2pix - Image-to-Image Translation in PyTorch
Mask_RCNN - Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow
rich-text-to-image - Rich-Text-to-Image Generation
face-parsing.PyTorch - Using modified BiSeNet for face parsing in PyTorch
mmtracking - OpenMMLab Video Perception Toolbox. It supports Video Object Detection (VID), Multiple Object Tracking (MOT), Single Object Tracking (SOT), Video Instance Segmentation (VIS) with a unified framework.
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.