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<div align="center">
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# Kneron AI Training/Deployment Platform (mmsegmentation-based)
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<img src="resources/mmseg-logo.png" width="600"/>
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<div> </div>
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<div align="center">
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<b><font size="5">OpenMMLab website</font></b>
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<sup>
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<a href="https://openmmlab.com">
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<i><font size="4">HOT</font></i>
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</a>
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</sup>
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<b><font size="5">OpenMMLab platform</font></b>
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<sup>
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<a href="https://platform.openmmlab.com">
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<i><font size="4">TRY IT OUT</font></i>
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</a>
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</sup>
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</div>
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<div> </div>
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</div>
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<br />
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[](https://pypi.org/project/mmsegmentation/)
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[](https://pypi.org/project/mmsegmentation)
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[](https://mmsegmentation.readthedocs.io/en/latest/)
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[](https://github.com/open-mmlab/mmsegmentation/actions)
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[](https://codecov.io/gh/open-mmlab/mmsegmentation)
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[](https://github.com/open-mmlab/mmsegmentation/blob/master/LICENSE)
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[](https://github.com/open-mmlab/mmsegmentation/issues)
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[](https://github.com/open-mmlab/mmsegmentation/issues)
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Documentation: https://mmsegmentation.readthedocs.io/
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English | [简体中文](README_zh-CN.md)
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## Introduction
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## Introduction
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MMSegmentation is an open source semantic segmentation toolbox based on PyTorch.
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[kneron-mmsegmentation](https://github.com/kneron/kneron-mmsegmentation) is a platform built upon the well-known [mmsegmentation](https://github.com/open-mmlab/mmsegmentation) for mmsegmentation. If you are looking for original mmsegmentation document, please visit [mmsegmentation docs](https://mmsegmentation.readthedocs.io/en/latest/) for detailed mmsegmentation usage.
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It is a part of the OpenMMLab project.
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The master branch works with **PyTorch 1.5+**.
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In this repository, we provide an end-to-end training/deployment flow to realize on Kneron's AI accelerators:
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1. **Training/Evalulation:**
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- Modified model configuration file and verified for Kneron hardware platform
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### Major features
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- Please see [Overview of Benchmark and Model Zoo](#Overview-of-Benchmark-and-Model-Zoo) for Kneron-Verified model list
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2. **Converting to ONNX:**
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- **Unified Benchmark**
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- tools/pytorch2onnx_kneron.py (beta)
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- Export *optimized* and *Kneron-toolchain supported* onnx
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We provide a unified benchmark toolbox for various semantic segmentation methods.
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- Automatically modify model for arbitrary data normalization preprocess
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3. **Evaluation**
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- **Modular Design**
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- tools/test_kneron.py (beta)
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- Evaluate the model with *pytorch checkpoint, onnx, and kneron-nef*
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We decompose the semantic segmentation framework into different components and one can easily construct a customized semantic segmentation framework by combining different modules.
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4. **Testing**
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- inference_kn (beta)
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- **Support of multiple methods out of box**
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- Verify the converted [NEF](http://doc.kneron.com/docs/#toolchain/manual/#5-nef-workflow) model on Kneron USB accelerator with this API
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5. **Converting Kneron-NEF:** (toolchain feature)
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The toolbox directly supports popular and contemporary semantic segmentation frameworks, *e.g.* PSPNet, DeepLabV3, PSANet, DeepLabV3+, etc.
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- Convert the trained pytorch model to [Kneron-NEF](http://doc.kneron.com/docs/#toolchain/manual/#5-nef-workflow) model, which could be used on Kneron hardware platform.
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- **High efficiency**
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The training speed is faster than or comparable to other codebases.
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## License
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## License
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@ -66,132 +29,40 @@ This project is released under the [Apache 2.0 license](LICENSE).
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## Changelog
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## Changelog
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v0.21.0 was released in 2/9/2022.
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N/A
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Please refer to [changelog.md](docs/en/changelog.md) for details and release history.
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## Benchmark and model zoo
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## Overview of Benchmark and Kneron Model Zoo
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Results and models are available in the [model zoo](docs/en/model_zoo.md).
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| Backbone | Crop Size | Mem (GB) | mIoU | Config | Download |
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|:--------:|:---------:|:--------:|:----:|:------:|:--------:|
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Supported backbones:
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| STDC 1 | 512x1024 | 7.15 | 69.29|[config](https://github.com/kneron/kneron-mmsegmentation/tree/master/configs/stdc/kn_stdc1_in1k-pre_512x1024_80k_cityscapes.py)|[model](https://github.com/kneron/Model_Zoo/blob/main/mmsegmentation/stdc_1/latest.zip)
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- [x] ResNet (CVPR'2016)
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- [x] ResNeXt (CVPR'2017)
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- [x] [HRNet (CVPR'2019)](configs/hrnet)
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- [x] [ResNeSt (ArXiv'2020)](configs/resnest)
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- [x] [MobileNetV2 (CVPR'2018)](configs/mobilenet_v2)
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- [x] [MobileNetV3 (ICCV'2019)](configs/mobilenet_v3)
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- [x] [Vision Transformer (ICLR'2021)](configs/vit)
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- [x] [Swin Transformer (ICCV'2021)](configs/swin)
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- [x] [Twins (NeurIPS'2021)](configs/twins)
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Supported methods:
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- [x] [FCN (CVPR'2015/TPAMI'2017)](configs/fcn)
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- [x] [ERFNet (T-ITS'2017)](configs/erfnet)
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- [x] [UNet (MICCAI'2016/Nat. Methods'2019)](configs/unet)
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- [x] [PSPNet (CVPR'2017)](configs/pspnet)
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- [x] [DeepLabV3 (ArXiv'2017)](configs/deeplabv3)
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- [x] [BiSeNetV1 (ECCV'2018)](configs/bisenetv1)
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- [x] [PSANet (ECCV'2018)](configs/psanet)
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- [x] [DeepLabV3+ (CVPR'2018)](configs/deeplabv3plus)
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- [x] [UPerNet (ECCV'2018)](configs/upernet)
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- [x] [ICNet (ECCV'2018)](configs/icnet)
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- [x] [NonLocal Net (CVPR'2018)](configs/nonlocal_net)
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- [x] [EncNet (CVPR'2018)](configs/encnet)
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- [x] [Semantic FPN (CVPR'2019)](configs/sem_fpn)
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- [x] [DANet (CVPR'2019)](configs/danet)
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- [x] [APCNet (CVPR'2019)](configs/apcnet)
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- [x] [EMANet (ICCV'2019)](configs/emanet)
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- [x] [CCNet (ICCV'2019)](configs/ccnet)
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- [x] [DMNet (ICCV'2019)](configs/dmnet)
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- [x] [ANN (ICCV'2019)](configs/ann)
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- [x] [GCNet (ICCVW'2019/TPAMI'2020)](configs/gcnet)
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- [x] [FastFCN (ArXiv'2019)](configs/fastfcn)
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- [x] [Fast-SCNN (ArXiv'2019)](configs/fastscnn)
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- [x] [ISANet (ArXiv'2019/IJCV'2021)](configs/isanet)
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- [x] [OCRNet (ECCV'2020)](configs/ocrnet)
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- [x] [DNLNet (ECCV'2020)](configs/dnlnet)
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- [x] [PointRend (CVPR'2020)](configs/point_rend)
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- [x] [CGNet (TIP'2020)](configs/cgnet)
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- [x] [BiSeNetV2 (IJCV'2021)](configs/bisenetv2)
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- [x] [STDC (CVPR'2021)](configs/stdc)
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- [x] [SETR (CVPR'2021)](configs/setr)
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- [x] [DPT (ArXiv'2021)](configs/dpt)
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- [x] [Segmenter (ICCV'2021)](configs/segmenter)
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- [x] [SegFormer (NeurIPS'2021)](configs/segformer)
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Supported datasets:
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- [x] [Cityscapes](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#cityscapes)
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- [x] [PASCAL VOC](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#pascal-voc)
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- [x] [ADE20K](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#ade20k)
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- [x] [Pascal Context](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#pascal-context)
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- [x] [COCO-Stuff 10k](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#coco-stuff-10k)
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- [x] [COCO-Stuff 164k](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#coco-stuff-164k)
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- [x] [CHASE_DB1](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#chase-db1)
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- [x] [DRIVE](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#drive)
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- [x] [HRF](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#hrf)
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- [x] [STARE](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#stare)
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- [x] [Dark Zurich](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#dark-zurich)
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- [x] [Nighttime Driving](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#nighttime-driving)
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- [x] [LoveDA](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#loveda)
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- [x] [Potsdam](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#isprs-potsdam)
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- [x] [Vaihingen](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#isprs-vaihingen)
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## Installation
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## Installation
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- Please refer to the Step 1 of [docs_kneron/stdc_step_by_step.md#step-1-environment](docs_kneron/stdc_step_by_step.md) for installation.
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- Please refer to [Kneron PLUS - Python: Installation](http://doc.kneron.com/docs/#plus_python/introduction/install_dependency/) for the environment setup for Kneron USB accelerator.
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Please refer to [get_started.md](docs/en/get_started.md#installation) for installation and [dataset_prepare.md](docs/en/dataset_prepare.md#prepare-datasets) for dataset preparation.
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## Getting Started
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### Tutorial - Kneron Edition
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- [STDC-Seg: Step-By-Step](docs_kneron/stdc_step_by_step.md): A tutorial for users to get started easily. To see detailed documents, please see below.
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## Get Started
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### Documents - Kneron Edition
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- [Kneron ONNX Export] (under development)
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- [Kneron Inference] (under development)
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- [Kneron Toolchain Step-By-Step (YOLOv3)](http://doc.kneron.com/docs/#toolchain/yolo_example/)
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- [Kneron Toolchain Manual](http://doc.kneron.com/docs/#toolchain/manual/#0-overview)
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Please see [train.md](docs/en/train.md) and [inference.md](docs/en/inference.md) for the basic usage of MMSegmentation.
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### Original mmsegmentation Documents
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There are also tutorials for [customizing dataset](docs/en/tutorials/customize_datasets.md), [designing data pipeline](docs/en/tutorials/data_pipeline.md), [customizing modules](docs/en/tutorials/customize_models.md), and [customizing runtime](docs/en/tutorials/customize_runtime.md).
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- [Original mmsegmentation getting started](https://github.com/open-mmlab/mmsegmentation#getting-started): It is recommended to read the original mmsegmentation getting started documents for other mmsegmentation operations.
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We also provide many [training tricks](docs/en/tutorials/training_tricks.md) for better training and [useful tools](docs/en/useful_tools.md) for deployment.
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- [Original mmsegmentation readthedoc](https://mmsegmentation.readthedocs.io/en/latest/): Original mmsegmentation documents.
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A Colab tutorial is also provided. You may preview the notebook [here](demo/MMSegmentation_Tutorial.ipynb) or directly [run](https://colab.research.google.com/github/open-mmlab/mmsegmentation/blob/master/demo/MMSegmentation_Tutorial.ipynb) on Colab.
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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{mmseg2020,
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title={{MMSegmentation}: OpenMMLab Semantic Segmentation Toolbox and Benchmark},
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author={MMSegmentation Contributors},
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howpublished = {\url{https://github.com/open-mmlab/mmsegmentation}},
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year={2020}
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}
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```
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## Contributing
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## Contributing
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[kneron-mmsegmentation](https://github.com/kneron/kneron-mmsegmentation) a platform built upon [OpenMMLab-mmsegmentation](https://github.com/open-mmlab/mmsegmentation)
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We appreciate all contributions to improve MMSegmentation. Please refer to [CONTRIBUTING.md](.github/CONTRIBUTING.md) for the contributing guideline.
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- For issues regarding to the original [mmsegmentation](https://github.com/open-mmlab/mmsegmentation):
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We appreciate all contributions to improve [OpenMMLab-mmsegmentation](https://github.com/open-mmlab/mmsegmentation). Ongoing projects can be found in out [GitHub Projects](https://github.com/open-mmlab/mmsegmentation/projects). Welcome community users to participate in these projects. Please refer to [CONTRIBUTING.md](.github/CONTRIBUTING.md) for the contributing guideline.
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## Acknowledgement
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- For issues regarding to this repository [kneron-mmsegmentation](https://github.com/kneron/kneron-mmsegmentation): Welcome to leave the comment or submit pull requests here to improve kneron-mmsegmentation
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MMSegmentation is an open source project that welcome any contribution and feedback.
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We wish that the toolbox and benchmark could serve the growing research
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community by providing a flexible as well as standardized toolkit to reimplement existing methods
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and develop their own new semantic segmentation methods.
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## Projects in OpenMMLab
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## Related Projects
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- [kneron-mmdetection](https://github.com/kneron/kneron-mmdetection): Kneron training/deployment platform on [OpenMMLab - mmdetection](https://github.com/open-mmlab/mmdetection) object detection toolbox
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- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision.
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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): A powerful toolkit for generative models.
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- [MIM](https://github.com/open-mmlab/mim): MIM Installs OpenMMLab Packages.
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- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark.
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- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab few shot learning toolbox and benchmark.
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- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 3D human parametric model toolbox and benchmark.
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- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab self-supervised learning toolbox and benchmark.
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- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab Model Compression Toolbox and Benchmark.
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- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab Model Deployment Framework.
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README_zh-CN.md
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README_zh-CN.md
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<div align="center">
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<img src="resources/mmseg-logo.png" width="600"/>
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<div> </div>
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<div align="center">
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<b><font size="5">OpenMMLab 官网</font></b>
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<sup>
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<a href="https://openmmlab.com">
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<i><font size="4">HOT</font></i>
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</a>
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</sup>
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<b><font size="5">OpenMMLab 开放平台</font></b>
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<sup>
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<a href="https://platform.openmmlab.com">
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<i><font size="4">TRY IT OUT</font></i>
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</a>
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</sup>
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</div>
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<div> </div>
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</div>
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<br />
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[](https://pypi.org/project/mmsegmentation/)
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[](https://pypi.org/project/mmsegmentation)
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[](https://mmsegmentation.readthedocs.io/zh_CN/latest/)
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[](https://github.com/open-mmlab/mmsegmentation/actions)
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[](https://codecov.io/gh/open-mmlab/mmsegmentation)
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[](https://github.com/open-mmlab/mmsegmentation/blob/master/LICENSE)
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[](https://github.com/open-mmlab/mmsegmentation/issues)
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[](https://github.com/open-mmlab/mmsegmentation/issues)
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文档: https://mmsegmentation.readthedocs.io/zh_CN/latest
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[English](README.md) | 简体中文
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## 简介
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MMSegmentation 是一个基于 PyTorch 的语义分割开源工具箱。它是 OpenMMLab 项目的一部分。
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主分支代码目前支持 PyTorch 1.5 以上的版本。
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### 主要特性
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- **统一的基准平台**
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我们将各种各样的语义分割算法集成到了一个统一的工具箱，进行基准测试。
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- **模块化设计**
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MMSegmentation 将分割框架解耦成不同的模块组件，通过组合不同的模块组件，用户可以便捷地构建自定义的分割模型。
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- **丰富的即插即用的算法和模型**
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MMSegmentation 支持了众多主流的和最新的检测算法，例如 PSPNet，DeepLabV3，PSANet，DeepLabV3+ 等.
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- **速度快**
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训练速度比其他语义分割代码库更快或者相当。
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## 开源许可证
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该项目采用 [Apache 2.0 开源许可证](LICENSE)。
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## 更新日志
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最新版本 v0.21.1 在 2022.2.9 发布。
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如果想了解更多版本更新细节和历史信息，请阅读[更新日志](docs/en/changelog.md)。
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## 基准测试和模型库
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测试结果和模型可以在[模型库](docs/zh_cn/model_zoo.md)中找到。
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已支持的骨干网络：
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- [x] ResNet (CVPR'2016)
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- [x] ResNeXt (CVPR'2017)
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- [x] [HRNet (CVPR'2019)](configs/hrnet)
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- [x] [ResNeSt (ArXiv'2020)](configs/resnest)
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- [x] [MobileNetV2 (CVPR'2018)](configs/mobilenet_v2)
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- [x] [MobileNetV3 (ICCV'2019)](configs/mobilenet_v3)
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- [x] [Vision Transformer (ICLR'2021)](configs/vit)
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- [x] [Swin Transformer (ICCV'2021)](configs/swin)
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- [x] [Twins (NeurIPS'2021)](configs/twins)
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已支持的算法：
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|
|
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- [x] [FCN (CVPR'2015/TPAMI'2017)](configs/fcn)
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- [x] [ERFNet (T-ITS'2017)](configs/erfnet)
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|
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- [x] [UNet (MICCAI'2016/Nat. Methods'2019)](configs/unet)
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|
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- [x] [PSPNet (CVPR'2017)](configs/pspnet)
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|
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- [x] [DeepLabV3 (ArXiv'2017)](configs/deeplabv3)
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|
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- [x] [BiSeNetV1 (ECCV'2018)](configs/bisenetv1)
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|
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- [x] [PSANet (ECCV'2018)](configs/psanet)
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|
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- [x] [DeepLabV3+ (CVPR'2018)](configs/deeplabv3plus)
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- [x] [UPerNet (ECCV'2018)](configs/upernet)
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|
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- [x] [ICNet (ECCV'2018)](configs/icnet)
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|
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- [x] [NonLocal Net (CVPR'2018)](configs/nonlocal_net)
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|
||||||
- [x] [EncNet (CVPR'2018)](configs/encnet)
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|
||||||
- [x] [Semantic FPN (CVPR'2019)](configs/sem_fpn)
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|
||||||
- [x] [DANet (CVPR'2019)](configs/danet)
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|
||||||
- [x] [APCNet (CVPR'2019)](configs/apcnet)
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|
||||||
- [x] [EMANet (ICCV'2019)](configs/emanet)
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|
||||||
- [x] [CCNet (ICCV'2019)](configs/ccnet)
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|
||||||
- [x] [DMNet (ICCV'2019)](configs/dmnet)
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|
||||||
- [x] [ANN (ICCV'2019)](configs/ann)
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|
||||||
- [x] [GCNet (ICCVW'2019/TPAMI'2020)](configs/gcnet)
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|
||||||
- [x] [FastFCN (ArXiv'2019)](configs/fastfcn)
|
|
||||||
- [x] [Fast-SCNN (ArXiv'2019)](configs/fastscnn)
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|
||||||
- [x] [ISANet (ArXiv'2019/IJCV'2021)](configs/isanet)
|
|
||||||
- [x] [OCRNet (ECCV'2020)](configs/ocrnet)
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|
||||||
- [x] [DNLNet (ECCV'2020)](configs/dnlnet)
|
|
||||||
- [x] [PointRend (CVPR'2020)](configs/point_rend)
|
|
||||||
- [x] [CGNet (TIP'2020)](configs/cgnet)
|
|
||||||
- [x] [BiSeNetV2 (IJCV'2021)](configs/bisenetv2)
|
|
||||||
- [x] [STDC (CVPR'2021)](configs/stdc)
|
|
||||||
- [x] [SETR (CVPR'2021)](configs/setr)
|
|
||||||
- [x] [DPT (ArXiv'2021)](configs/dpt)
|
|
||||||
- [x] [Segmenter (ICCV'2021)](configs/segmenter)
|
|
||||||
- [x] [SegFormer (NeurIPS'2021)](configs/segformer)
|
|
||||||
|
|
||||||
已支持的数据集：
|
|
||||||
|
|
||||||
- [x] [Cityscapes](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#cityscapes)
|
|
||||||
- [x] [PASCAL VOC](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#pascal-voc)
|
|
||||||
- [x] [ADE20K](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#ade20k)
|
|
||||||
- [x] [Pascal Context](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#pascal-context)
|
|
||||||
- [x] [COCO-Stuff 10k](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#coco-stuff-10k)
|
|
||||||
- [x] [COCO-Stuff 164k](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#coco-stuff-164k)
|
|
||||||
- [x] [CHASE_DB1](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#chase-db1)
|
|
||||||
- [x] [DRIVE](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#drive)
|
|
||||||
- [x] [HRF](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#hrf)
|
|
||||||
- [x] [STARE](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#stare)
|
|
||||||
- [x] [Dark Zurich](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#dark-zurich)
|
|
||||||
- [x] [Nighttime Driving](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#nighttime-driving)
|
|
||||||
- [x] [LoveDA](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#loveda)
|
|
||||||
- [x] [Potsdam](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#isprs-potsdam)
|
|
||||||
- [x] [Vaihingen](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#isprs-vaihingen)
|
|
||||||
|
|
||||||
## 安装
|
|
||||||
|
|
||||||
请参考[快速入门文档](docs/zh_cn/get_started.md#installation)进行安装，参考[数据集准备](docs/zh_cn/dataset_prepare.md)处理数据。
|
|
||||||
|
|
||||||
## 快速入门
|
|
||||||
|
|
||||||
请参考[训练教程](docs/zh_cn/train.md)和[测试教程](docs/zh_cn/inference.md)学习 MMSegmentation 的基本使用。
|
|
||||||
我们也提供了一些进阶教程，内容覆盖了[增加自定义数据集](docs/zh_cn/tutorials/customize_datasets.md)，[设计新的数据预处理流程](docs/zh_cn/tutorials/data_pipeline.md)，[增加自定义模型](docs/zh_cn/tutorials/customize_models.md)，[增加自定义的运行时配置](docs/zh_cn/tutorials/customize_runtime.md)。
|
|
||||||
除此之外，我们也提供了很多实用的[训练技巧说明](docs/zh_cn/tutorials/training_tricks.md)和模型部署相关的[有用的工具](docs/zh_cn/useful_tools.md)。
|
|
||||||
|
|
||||||
同时，我们提供了 Colab 教程。你可以在[这里](demo/MMSegmentation_Tutorial.ipynb)浏览教程，或者直接在 Colab 上[运行](https://colab.research.google.com/github/open-mmlab/mmsegmentation/blob/master/demo/MMSegmentation_Tutorial.ipynb)。
|
|
||||||
|
|
||||||
## 引用
|
|
||||||
|
|
||||||
如果你觉得本项目对你的研究工作有所帮助，请参考如下 bibtex 引用 MMSegmentation。
|
|
||||||
|
|
||||||
```bibtex
|
|
||||||
@misc{mmseg2020,
|
|
||||||
title={{MMSegmentation}: OpenMMLab Semantic Segmentation Toolbox and Benchmark},
|
|
||||||
author={MMSegmentation Contributors},
|
|
||||||
howpublished = {\url{https://github.com/open-mmlab/mmsegmentation}},
|
|
||||||
year={2020}
|
|
||||||
}
|
|
||||||
```
|
|
||||||
|
|
||||||
## 贡献指南
|
|
||||||
|
|
||||||
我们感谢所有的贡献者为改进和提升 MMSegmentation 所作出的努力。请参考[贡献指南](.github/CONTRIBUTING.md)来了解参与项目贡献的相关指引。
|
|
||||||
|
|
||||||
## 致谢
|
|
||||||
|
|
||||||
MMSegmentation 是一个由来自不同高校和企业的研发人员共同参与贡献的开源项目。我们感谢所有为项目提供算法复现和新功能支持的贡献者，以及提供宝贵反馈的用户。 我们希望这个工具箱和基准测试可以为社区提供灵活的代码工具，供用户复现已有算法并开发自己的新模型，从而不断为开源社区提供贡献。
|
|
||||||
|
|
||||||
## OpenMMLab 的其他项目
|
|
||||||
|
|
||||||
- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab 计算机视觉基础库
|
|
||||||
- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab 图像分类工具箱
|
|
||||||
- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab 目标检测工具箱
|
|
||||||
- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab 新一代通用 3D 目标检测平台
|
|
||||||
- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab 语义分割工具箱
|
|
||||||
- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab 新一代视频理解工具箱
|
|
||||||
- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab 一体化视频目标感知平台
|
|
||||||
- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab 姿态估计工具箱
|
|
||||||
- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab 图像视频编辑工具箱
|
|
||||||
- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab 全流程文字检测识别理解工具包
|
|
||||||
- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab 生成模型工具箱
|
|
||||||
- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab 光流估计工具箱与测试基准
|
|
||||||
- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab 少样本学习工具箱与测试基准
|
|
||||||
- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 人体参数化模型工具箱与测试基准
|
|
||||||
- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab 自监督学习工具箱与测试基准
|
|
||||||
- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab 模型压缩工具箱与测试基准
|
|
||||||
- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab 模型部署框架
|
|
||||||
|
|
||||||
## 欢迎加入 OpenMMLab 社区
|
|
||||||
|
|
||||||
扫描下方的二维码可关注 OpenMMLab 团队的 [知乎官方账号](https://www.zhihu.com/people/openmmlab)，加入 [OpenMMLab 团队](https://jq.qq.com/?_wv=1027&k=aCvMxdr3) 以及 [MMSegmentation](https://jq.qq.com/?_wv=1027&k=ukevz6Ie) 的 QQ 群。
|
|
||||||
|
|
||||||
<div align="center">
|
|
||||||
<img src="docs/zh_cn/imgs/zhihu_qrcode.jpg" height="400" /> <img src="docs/zh_cn/imgs/qq_group_qrcode.jpg" height="400" /> <img src="docs/zh_cn/imgs/seggroup_qrcode.jpg" height="400" />
|
|
||||||
</div>
|
|
||||||
|
|
||||||
我们会在 OpenMMLab 社区为大家
|
|
||||||
|
|
||||||
- 📢 分享 AI 框架的前沿核心技术
|
|
||||||
- 💻 解读 PyTorch 常用模块源码
|
|
||||||
- 📰 发布 OpenMMLab 的相关新闻
|
|
||||||
- 🚀 介绍 OpenMMLab 开发的前沿算法
|
|
||||||
- 🏃 获取更高效的问题答疑和意见反馈
|
|
||||||
- 🔥 提供与各行各业开发者充分交流的平台
|
|
||||||
|
|
||||||
干货满满 📘，等你来撩 💗，OpenMMLab 社区期待您的加入 👬
|
|
||||||
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