FastDeploy
mmdeploy
| FastDeploy | mmdeploy | |
|---|---|---|
| 5 | 4 | |
| 2,705 | 2,511 | |
| 4.3% | 4.2% | |
| 7.5 | 7.9 | |
| 12 days ago | about 1 month ago | |
| C++ | 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.
FastDeploy
- Testing YOLO on Orange Pi 5
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Github-Paddle.js: Run AI models on browsers for computer version.
Refer to this link(https://github.com/PaddlePaddle/FastDeploy/blob/develop/examples/application/js/WebDemo_en.md) for examples and tutorials.
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[P] FastDeploy: Make DL deployment easier and faster!
🔥 2022.10.31：Release FastDeploy release v0.5.0
Repo: https://github.com/PaddlePaddle/FastDeploy
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?
TNN - TNN: developed by Tencent Youtu Lab and Guangying Lab, a uniform deep learning inference framework for mobile、desktop and server. TNN is distinguished by several outstanding features, including its cross-platform capability, high performance, model compression and code pruning. Based on ncnn and Rapidnet, TNN further strengthens the support and performance optimization for mobile devices, and also draws on the advantages of good extensibility and high performance from existed open source efforts. TNN has been deployed in multiple Apps from Tencent, such as Mobile QQ, Weishi, Pitu, etc. Contributions are welcome to work in collaborative with us and make TNN a better framework.
mmflow - OpenMMLab optical flow toolbox and benchmark
tensorRT_Pro - C++ library based on tensorrt integration
mmfewshot - OpenMMLab FewShot Learning Toolbox and Benchmark
deepdetect - Deep Learning API and Server in C++14 support for Caffe, PyTorch,TensorRT, Dlib, NCNN, Tensorflow, XGBoost and TSNE
mmdetection - OpenMMLab Detection Toolbox and Benchmark
jetson-inference - Hello AI World guide to deploying deep-learning inference networks and deep vision primitives with TensorRT and NVIDIA Jetson.
mmpretrain - OpenMMLab Pre-training Toolbox and Benchmark
maps-core - The lightweight and modern Map SDK for Android and iOS
mmrotate - OpenMMLab Rotated Object Detection Toolbox and Benchmark
useful-transformers - Efficient Inference of Transformer models
mmocr - OpenMMLab Text Detection, Recognition and Understanding Toolbox