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[Docs] Refactor documentations (#373)
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* merge en & zh_cn docs

* fix error links

* Update stat.py

* Update stat.py
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.scrapy

# Sphinx documentation
docs/_build/
docs/en/_build/
docs/zh_cn/_build/

# PyBuilder
target/
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10 changes: 5 additions & 5 deletions README.md
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Expand Up @@ -49,11 +49,11 @@ This project is released under the [Apache 2.0 license](LICENSE).
## Changelog

v0.8.0 was released in 03/10/2021.
Please refer to [changelog.md](docs/changelog.md) for details and release history.
Please refer to [changelog.md](docs/en/changelog.md) for details and release history.

## Benchmark and model zoo

Results and models are available in the [model zoo](docs/model_zoo.md).
Results and models are available in the [model zoo](docs/en/model_zoo.md).

Supported methods of video object detection:

Expand All @@ -79,12 +79,12 @@ Supported methods of video instance segmentation:

## Installation

Please refer to [install.md](docs/install.md) for install instructions.
Please refer to [install.md](docs/en/install.md) for install instructions.

## Getting Started

Please see [dataset.md](docs/dataset.md) and [quick_run.md](docs/quick_run.md) for the basic usage of MMTracking.
We also provide usage [tutorials](docs/tutorials/), such as [learning about configs](docs/tutorials/config.md), [an example about detailed description of vid config](docs/tutorials/config_vid.md), [an example about detailed description of mot config](docs/tutorials/config_mot.md), [an example about detailed description of sot config](docs/tutorials/config_sot.md), [customizing dataset](docs/tutorials/customize_dataset.md), [customizing data pipeline](docs/tutorials/customize_data_pipeline.md), [customizing vid model](docs/tutorials/customize_vid_model.md), [customizing mot model](docs/tutorials/customize_mot_model.md), [customizing sot model](docs/tutorials/customize_sot_model.md), [customizing runtime settings](docs/tutorials/customize_runtime.md) and [useful tools](docs/useful_tools_scripts.md).
Please see [dataset.md](docs/en/dataset.md) and [quick_run.md](docs/en/quick_run.md) for the basic usage of MMTracking.
We also provide usage [tutorials](docs/en/tutorials/), such as [learning about configs](docs/en/tutorials/config.md), [an example about detailed description of vid config](docs/en/tutorials/config_vid.md), [an example about detailed description of mot config](docs/en/tutorials/config_mot.md), [an example about detailed description of sot config](docs/en/tutorials/config_sot.md), [customizing dataset](docs/en/tutorials/customize_dataset.md), [customizing data pipeline](docs/en/tutorials/customize_data_pipeline.md), [customizing vid model](docs/en/tutorials/customize_vid_model.md), [customizing mot model](docs/en/tutorials/customize_mot_model.md), [customizing sot model](docs/en/tutorials/customize_sot_model.md), [customizing runtime settings](docs/en/tutorials/customize_runtime.md) and [useful tools](docs/en/useful_tools_scripts.md).

## Contributing

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Expand Up @@ -47,11 +47,11 @@ MMTracking是一款基于PyTorch的视频目标感知开源工具箱,是[OpenM

## 更新记录

v0.8.0版本已于2021年10月03日发布,可通过查阅[更新日志](/docs/changelog.md)了解更多细节以及发布历史。
v0.8.0版本已于2021年10月03日发布,可通过查阅[更新日志](docs/en/changelog.md)了解更多细节以及发布历史。

## 基准测试与模型库

本工具箱支持的各个模型的结果和设置都可以在[模型库](docs/model_zoo.md)页面中查看。
本工具箱支持的各个模型的结果和设置都可以在[模型库](docs/en/model_zoo.md)页面中查看。

支持的视频目标检测算法:

Expand All @@ -77,11 +77,11 @@ v0.8.0版本已于2021年10月03日发布,可通过查阅[更新日志](/docs/

## 安装

请参考[安装指南](docs/install.md)进行安装。
请参考[安装指南](docs/en/install.md)进行安装。

## 开始使用MMTracking

请参考[数据集](docs/dataset.md)[快速开始](docs/quick_run.md)了解MMTracking的基本使用。MMTracking也提供了更详细的[教程](docs/tutorials/),比如[配置文件简介](docs/tutorials/config.md), [视频目标检测器配置文件详解](docs/tutorials/config_vid.md), [多目标跟踪器配置文件详解](docs/tutorials/config_mot.md), [单目标跟踪器配置文件详解](docs/tutorials/config_sot.md), [自定义数据集](docs/tutorials/customize_dataset.md), [自定义数据预处理流程](docs/tutorials/customize_data_pipeline.md), [自定义视频目标检测器](docs/tutorials/customize_vid_model.md), [自定义多目标跟踪器](docs/tutorials/customize_mot_model.md), [自定义单目标跟踪器](docs/tutorials/customize_sot_model.md), [自定义训练配置](docs/tutorials/customize_runtime.md) 以及 [有用的工具和脚本](docs/useful_tools_scripts.md).
请参考[数据集](docs/en/dataset.md)[快速开始](docs/en/quick_run.md)了解MMTracking的基本使用。MMTracking也提供了更详细的[教程](docs/en/tutorials/),比如[配置文件简介](docs/en/tutorials/config.md), [视频目标检测器配置文件详解](docs/en/tutorials/config_vid.md), [多目标跟踪器配置文件详解](docs/en/tutorials/config_mot.md), [单目标跟踪器配置文件详解](docs/en/tutorials/config_sot.md), [自定义数据集](docs/en/tutorials/customize_dataset.md), [自定义数据预处理流程](docs/en/tutorials/customize_data_pipeline.md), [自定义视频目标检测器](docs/en/tutorials/customize_vid_model.md), [自定义多目标跟踪器](docs/en/tutorials/customize_mot_model.md), [自定义单目标跟踪器](docs/en/tutorials/customize_sot_model.md), [自定义训练配置](docs/en/tutorials/customize_runtime.md) 以及 [有用的工具和脚本](docs/en/useful_tools_scripts.md).

## 参与贡献

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## Training with configs

Please refer to [Training](../docs/quick_run.md#training) to see the tutorials of training models.
Please refer to [Training](../docs/en/quick_run.md#training) to see the tutorials of training models.

## Testing with configs

Please refer to [Testing](../docs/quick_run.md#testing) to see the tutorials of testing models.
Please refer to [Testing](../docs/en/quick_run.md#testing) to see the tutorials of testing models.

## Inference with configs

Please refer to [Inference](../docs/quick_run.md#inference) to see the tutorials of inferencing models.
Please refer to [Inference](../docs/en/quick_run.md#inference) to see the tutorials of inferencing models.
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## Training with MOT configs

Please refer to [Train MOT models](../../docs/quick_run.md#examples-of-training-mot-model) to see the examples.
Please refer to [Train MOT models](../../docs/en/quick_run.md#examples-of-training-mot-model) to see the examples.

## Testing with MOT configs

Please refer to [Test MOT models](../../docs/quick_run.md#examples-of-testing-mot-model) to see the examples.
Please refer to [Test MOT models](../../docs/en/quick_run.md#examples-of-testing-mot-model) to see the examples.

## Inference with MOT configs

Please refer to [Inference MOT models](../../docs/quick_run.md#inference-motvis-models) to see the examples.
Please refer to [Inference MOT models](../../docs/en/quick_run.md#inference-motvis-models) to see the examples.
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## Training with SOT configs

Please refer to [Train SOT models](../../docs/quick_run.md#examples-of-training-sot-model) to see the examples.
Please refer to [Train SOT models](../../docs/en/quick_run.md#examples-of-training-sot-model) to see the examples.

## Testing with SOT configs

Please refer to [Test SOT models](../../docs/quick_run.md#examples-of-testing-sot-model) to see the examples.
Please refer to [Test SOT models](../../docs/en/quick_run.md#examples-of-testing-sot-model) to see the examples.

## Inference with SOT configs

Please refer to [Inference SOT models](../../docs/quick_run.md#inference-sot-models) to see the examples.
Please refer to [Inference SOT models](../../docs/en/quick_run.md#inference-sot-models) to see the examples.
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The checkpoints from 10-th to 20-th epoch will be evaluated during training. You can find the best checkpoint from the log file.

If you want to get better results, you can use the best checkpoint to search the hyperparameters on UAV123 following [here](https://github.com/open-mmlab/mmtracking/blob/master/docs/useful_tools_scripts.md#siameserpn-test-time-parameter-search).
If you want to get better results, you can use the best checkpoint to search the hyperparameters on UAV123 following [here](https://github.com/open-mmlab/mmtracking/blob/master/docs/en/useful_tools_scripts.md#siameserpn-test-time-parameter-search).
Experimentally, the hyperparameters search on UAV123 can bring around 1.0 Success gain.

The results below are achieved without hyperparameters search.
Expand All @@ -68,7 +68,7 @@ The results of SiameseRPN++ in TrackingNet are reimplemented by ourselves. The b

The checkpoints from 10-th to 20-th epoch will be evaluated during training. You can find the best checkpoint from the log file.

If you want to get better results, you can use the best checkpoint to search the hyperparameters on OTB100 following [here](https://github.com/open-mmlab/mmtracking/blob/master/docs/useful_tools_scripts.md#siameserpn-test-time-parameter-search). Experimentally, the hyperparameters search on OTB100 can bring around 1.0 Success gain.
If you want to get better results, you can use the best checkpoint to search the hyperparameters on OTB100 following [here](https://github.com/open-mmlab/mmtracking/blob/master/docs/en/useful_tools_scripts.md#siameserpn-test-time-parameter-search). Experimentally, the hyperparameters search on OTB100 can bring around 1.0 Success gain.

**Note:** The results reported in the paper are 69.6 Success and 91.4 Precision. We train the SiameseRPN++ in the official [pysot](https://github.com/STVIR/pysot) codebase and can not reproduce the same results. We only get 66.1 Success and 86.7 Precision by following the training and hyperparameters searching instructions of pysot, which are lower than those of the paper by 3.5 Succuess and 4.7 Precision respectively. Without hyperparameters search, we get 65.3 Success and 85.8 Precision. In our codebase, the results below are also achieved without hyperparameters search, close to the results reproduced in pysot in the same setting.

Expand All @@ -80,7 +80,7 @@ If you want to get better results, you can use the best checkpoint to search the

The checkpoints from 10-th to 20-th epoch will be evaluated during training. You can find the best checkpoint from the log file.

If you want to get better results, you can use the best checkpoint to search the hyperparameters on VOT2018 following [here](https://github.com/open-mmlab/mmtracking/blob/master/docs/useful_tools_scripts.md#siameserpn-test-time-parameter-search).
If you want to get better results, you can use the best checkpoint to search the hyperparameters on VOT2018 following [here](https://github.com/open-mmlab/mmtracking/blob/master/docs/en/useful_tools_scripts.md#siameserpn-test-time-parameter-search).

**Note:** The result reported in the paper is 0.414 EAO. We train the SiameseRPN++ in the official [pysot](https://github.com/STVIR/pysot) codebase and can not reproduce the same result. We only get 0.364 EAO by following the training and hyperparameters searching instructions of pysot, which is lower than that of the paper by 0.05 EAO. Without hyperparameters search, we get 0.346 EAO. In our codebase, the results below are also achieved without hyperparameters search, close to the results reproduced in pysot in the same setting.

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## Training with VID configs

Please refer to [Train VID models](../../docs/quick_run.md#examples-of-training-vid-model) to see the examples.
Please refer to [Train VID models](../../docs/en/quick_run.md#examples-of-training-vid-model) to see the examples.

## Testing with VID configs

Please refer to [Test VID models](../../docs/quick_run.md#examples-of-testing-vid-model) to see the examples.
Please refer to [Test VID models](../../docs/en/quick_run.md#examples-of-testing-vid-model) to see the examples.

## Inference with VID configs

Please refer to [Inference VID models](../../docs/quick_run.md#inference-vid-models) to see the examples.
Please refer to [Inference VID models](../../docs/en/quick_run.md#inference-vid-models) to see the examples.
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## Training with VIS configs

Please refer to [Train VIS models](../../docs/quick_run.md#examples-of-training-vis-model) to see the examples.
Please refer to [Train VIS models](../../docs/en/quick_run.md#examples-of-training-vis-model) to see the examples.

## Testing with VIS configs

Please refer to [Test VIS models](../../docs/quick_run.md#examples-of-testing-vis-model) to see the examples.
Please refer to [Test VIS models](../../docs/en/quick_run.md#examples-of-testing-vis-model) to see the examples.

## Inference with VIS configs

Please refer to [Inference VIS models](../../docs/quick_run.md#inference-motvis-models) to see the examples.
Please refer to [Inference VIS models](../../docs/en/quick_run.md#inference-motvis-models) to see the examples.
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# Demo

Please refer to the inference of [VID](../docs/quick_run.md#inference-vid-models), [MOT](../docs/quick_run.md#inference-motvis-models), [SOT](../docs/quick_run.md#inference-sot-models) and [VIS](../docs/quick_run.md#inference-motvis-models) to run demos.
Please refer to the inference of [VID](../docs/en/quick_run.md#inference-vid-models), [MOT](../docs/en/quick_run.md#inference-motvis-models), [SOT](../docs/en/quick_run.md#inference-sot-models) and [VIS](../docs/en/quick_run.md#inference-motvis-models) to run demos.
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import pytorch_sphinx_theme

sys.path.insert(0, os.path.abspath('..'))
sys.path.insert(0, os.path.abspath('../..'))

# -- Project information -----------------------------------------------------

project = 'MMTracking'
copyright = '2018-2021, OpenMMLab'
author = 'MMTracking Authors'
version_file = '../mmtrack/version.py'
version_file = '../../mmtrack/version.py'


def get_version():
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url_prefix = 'https://github.com/open-mmlab/mmtracking/blob/master/'

files = sorted(glob.glob('../configs/*/*/README.md'))
files = sorted(glob.glob('../../configs/*/*/README.md'))

stats = []
titles = []
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import pytorch_sphinx_theme

sys.path.insert(0, os.path.abspath('..'))
sys.path.insert(0, os.path.abspath('../..'))

# -- Project information -----------------------------------------------------

project = 'MMTracking'
copyright = '2018-2021, OpenMMLab'
author = 'MMTracking Authors'
version_file = '../mmtrack/version.py'
version_file = '../../mmtrack/version.py'


def get_version():
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url_prefix = 'https://github.com/open-mmlab/mmtracking/blob/master/'

files = sorted(glob.glob('../configs/*/*/README.md'))
files.extend(glob.glob('../configs/*/README.md'))
files = sorted(glob.glob('../../configs/*/*/README.md'))

stats = []
titles = []
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