mmagic
mmdeploy
| mmagic | mmdeploy | |
|---|---|---|
| 5 | 4 | |
| 6,588 | 2,511 | |
| 1.1% | 2.0% | |
| 8.7 | 7.9 | |
| about 2 months ago | about 1 month ago | |
| Jupyter Notebook | 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.
mmagic
- More than Editing, Unlock the Magic!
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MMEditing v1.0.0rc4 has been released （including Disco-Diffusion）
Join us to make it better! Try at https://github.com/open-mmlab/mmediting/tree/1.x
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MMDeploy: Deploy All the Algorithms of OpenMMLab
MMEditing: OpenMMLab image and video editing toolbox.
mmdeploy
- [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.
What are some alternatives?
a-PyTorch-Tutorial-to-Super-Resolution - Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network | a PyTorch Tutorial to Super-Resolution
FastDeploy - ⚡️An Easy-to-use and Fast Deep Learning Model Deployment Toolkit for ☁️Cloud 📱Mobile and 📹Edge. Including Image, Video, Text and Audio 20+ main stream scenarios and 150+ SOTA models with end-to-end optimization, multi-platform and multi-framework support.
Real-ESRGAN - Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration.
mmflow - OpenMMLab optical flow toolbox and benchmark
Image-Super-Resolution-via-Iterative-Refinement - Unofficial implementation of Image Super-Resolution via Iterative Refinement by Pytorch
mmfewshot - OpenMMLab FewShot Learning Toolbox and Benchmark
Real-ESRGAN-colab - A Real-ESRGAN model trained on a custom dataset
mmdetection - OpenMMLab Detection Toolbox and Benchmark
cnn-watermark-removal - Fully convolutional deep neural network to remove transparent overlays from images
mmpretrain - OpenMMLab Pre-training Toolbox and Benchmark
Deep-Exemplar-based-Video-Colorization - The source code of CVPR 2019 paper "Deep Exemplar-based Video Colorization".
mmrotate - OpenMMLab Rotated Object Detection Toolbox and Benchmark