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* Fix include and lib paths for onnxruntime. * Fixes for SSD export test * Add onnx2openvino and OpenVINODetector. Test models: ssd, retinanet, fcos, fsaf. * Add support for two-stage models: faster_rcnn, cascade_rcnn * Add doc * Add strip_doc_string for openvino. * Fix openvino preprocess. * Add OpenVINO to test_wrapper.py. * Fix * Add openvino_execute. * Removed preprocessing. * Fix onnxruntime cmake. * Rewrote postprocessing and forward, added docstrings and fixes. * Added device type change to OpenVINOWrapper. * Update forward_of_single_roi_extractor_dynamic_openvino and fix doc. * Update docs. * Add support for masks (Mask RCNN). * Add masks to CascadeRoIHead.simple_test. * Added masks to test_OpenVINODetector. * Added test_cascade_roi_head_with_mask. * Update docs. * Fix segm_results shape. * Fix TopK in NMS and add test_multiclass_nms_with_keep_top_k. * Removed unnecessary functions. * Fix. * Fix test_multiclass_nms_with_keep_top_k. * Updated test_OpenVINODetector.
85 lines
3.2 KiB
Markdown
85 lines
3.2 KiB
Markdown
## Introduction
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English | [简体中文](README_zh-CN.md)
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MMDeploy is an open-source deep learning model deployment toolset. It is
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a part of the [OpenMMLab](https://openmmlab.com/) project.
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### Major features
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- **OpenMMLab model support**
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Models in OpenMMLab can be deployed with this project. Such as MMClassification, MMDetection, etc.
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- **Multiple inference engine support**
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Models can be exported and run in different backends. Such as ONNX Runtime, TensorRT, etc.
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- **Model rewrite**
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Modules and functions used in models can be rewritten to meet the demand of different backends. It is easy to add new model support.
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## License
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This project is released under the [Apache 2.0 license](LICENSE).
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## Codebase and Backend support
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Supported codebase:
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- [x] MMClassification
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- [x] MMDetection
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- [x] MMSegmentation
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- [x] MMEditing
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- [x] MMOCR
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Supported backend:
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- [x] ONNX Runtime
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- [x] TensorRT
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- [x] PPL
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- [x] ncnn
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- [x] OpenVINO
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## Installation
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Please refer to [build.md](docs/build.md) for installation.
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## Getting Started
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Please read [how_to_convert_model.md](docs/tutorials/how_to_convert_model.md) for the basic usage of MMDeploy. There are also tutorials for [how to write config](docs/tutorials/how_to_write_config.md), [how to support new models](docs/tutorials/how_to_support_new_models.md) and [how to test model](docs/tutorials/how_to_test_model.md).
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Please refer to [FAQ](docs/faq.md) for frequently asked questions.
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## Citation
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If you find this project useful in your research, please consider cite:
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```BibTeX
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@misc{=mmdeploy,
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title={OpenMMLab's Model deployment toolbox.},
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author={MMDeploy Contributors},
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howpublished = {\url{https://github.com/open-mmlab/mmdeploy}},
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year={2021}
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}
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```
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## Contributing
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We appreciate all contributions to improve MMDeploy. Please refer to [CONTRIBUTING.md](.github/CONTRIBUTING.md) for the contributing guideline.
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## Projects in OpenMMLab
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- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision.
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- [MIM](https://github.com/open-mmlab/mim): MIM Installs OpenMMLab Packages.
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- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab image classification toolbox and benchmark.
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- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark.
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- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection.
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- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark.
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- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark.
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- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark.
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- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark.
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- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox.
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- [MMOCR](https://github.com/open-mmlab/mmocr): A Comprehensive Toolbox for Text Detection, Recognition and Understanding.
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- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab image and video generative models toolbox.
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