mmdeploy
OpenMMLab Model Deployment Framework (by open-mmlab)
mmsegmentation
OpenMMLab Semantic Segmentation Toolbox and Benchmark. (by open-mmlab)
| mmdeploy | mmsegmentation | |
|---|---|---|
| 4 | 7 | |
| 3,023 | 9,140 | |
| 1.0% | 0.6% | |
| 4.7 | 5.8 | |
| 11 months ago | about 1 year ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
mmdeploy
Posts with mentions or reviews of mmdeploy.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-06-28.
- [D] Object detection models that can be easily converted to CoreML
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Orange Pi 5 Plus Koboldcpp Demo (MPT, Falcon, Mini-Orca, Openllama)
The RK3588 also has a NPU for accelerating neural networks. The bad news is the API is not supported by any of the inference engines (afaik), but the NPU can run models directly that have been converted to the RKNN format. It is a long shot, but you can find details here.
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MMDeploy: Deploy All the Algorithms of OpenMMLab
BibTeX @misc{=mmdeploy, title={OpenMMLab's Model Deployment Toolbox.}, author={MMDeploy Contributors}, howpublished = {\url{https://github.com/open-mmlab/mmdeploy}}, year={2021} }
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Removing the bounding box generated by OnnxRuntime segmentation
I have a semantic segmentation model trained using the mmdetection repo. Then it is converted to the ONNX format using the mmdeploy repo.
mmsegmentation
Posts with mentions or reviews of mmsegmentation.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-03-06.
- [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?
When comparing mmdeploy and mmsegmentation you can also consider the following projects:
FastDeploy - High-performance Inference and Deployment Toolkit for LLMs and VLMs based on PaddlePaddle
Pytorch-UNet - PyTorch implementation of the U-Net for image semantic segmentation with high quality images
mmfewshot - OpenMMLab FewShot Learning Toolbox and Benchmark
segmentation_models.pytorch - Semantic segmentation models with 500+ pretrained convolutional and transformer-based backbones.
mmselfsup - OpenMMLab Self-Supervised Learning Toolbox and Benchmark
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.