CodeCamp #139 [Feature] Support REFUGE dataset. #2420
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CodeCamp open-mmlab#144 [Doc] Chinese version of config tutorial
Co-authored-by: 谢昕辰 <xiexinch@outlook.com>
CodeCamp open-mmlab#147 [Doc] Add Chinese version of train & test tutorial
Co-authored-by: 谢昕辰 <xiexinch@outlook.com>
Co-authored-by: 谢昕辰 <xiexinch@outlook.com>
MengzhangLI
added
Dataset
…dev-1.x_liuruiqiang
Update-2023-01-05The primary results of REFUGE dataset:
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MengzhangLI
reviewed
Jan 5, 2023
MengzhangLI
reviewed
Jan 9, 2023
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| First, registrate in [REFUGE2020Challenge](https://refuge.grand-challenge.org)and download data from [this link](https://refuge.grand-challenge.org/REFUGE2Download) | ||
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| To convert REFUGE dataset to MMSegmentation format, you should run the following command: |
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For example, in Synapes dataset, it shows:
│ ├── iSAID
│ │ ├── train
│ │ │ ├── images
│ │ │ │ ├── part1.zip
│ │ │ │ ├── part2.zip
│ │ │ │ ├── part3.zip
│ │ │ ├── Semantic_masks
│ │ │ │ ├── images.zip
│ │ ├── val
│ │ │ ├── images
│ │ │ │ ├── part1.zip
│ │ │ ├── Semantic_masks
│ │ │ │ ├── images.zip
│ │ ├── test
│ │ │ ├── images
│ │ │ │ ├── part1.zip
│ │ │ │ ├── part2.zip
MengzhangLI
reviewed
Jan 9, 2023
| To convert REFUGE dataset to MMSegmentation format, you should run the following command: | ||
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| ```shell | ||
| python tools/convert_datasets/refuge.py --raw_data_root=.. --tmp_dir=.. |
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Make REFUGE.zip which could be downloaded from official website as raw_data thus users could generate mmsegmentation-style data automatically.
Codecov ReportBase: 83.32% // Head: 83.42% // Increases project coverage by
Additional details and impacted files@@ Coverage Diff @@
## dev-1.x #2420 +/- ##
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+ Coverage 83.32% 83.42% +0.10%
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MeowZheng
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Jan 31, 2023
MeowZheng
reviewed
Jan 31, 2023
MeowZheng
approved these changes
Feb 1, 2023
| python tools/convert_datasets/refuge.py --raw_data_root=/path/to/refuge/REFUGE2/REFUGE2 | ||
| ``` | ||
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| 这个脚本将自动生成正确的文件夹结构。 |
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Suggested change
| 这个脚本将自动生成正确的文件夹结构。 | |
| 处理后的目录结构如下，该数据有400 张图片的训练集， 400 张图片的验证集和 400 张图片的测试集： |
also add an example for the dataset structure after precess
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| 这个脚本将自动生成正确的文件夹结构。 | ||
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| 使用我们默认的配置， 将生成 400 张图片的训练集， 400 张图片的验证集和 400 张图片的测试集. |
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Suggested change
| 使用我们默认的配置， 将生成 400 张图片的训练集， 400 张图片的验证集和 400 张图片的测试集. |
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| The script will make directory structure automatically. | ||
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| In our default setting, it will generate 400 images for training, 400 images for validation and 400 images for testing. |
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Suggested change
| The script will make directory structure automatically. | |
| In our default setting, it will generate 400 images for training, 400 images for validation and 400 images for testing. | |
| There is the dataset structure after running `tools/convert_datasets/refuge.py`, which includes 400 images for training, 400 images for validation and 400 images for testing. |
also add the proper dataset structure example
| │ ├── __MACOSX | ||
| ``` | ||
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| To convert REFUGE dataset to MMSegmentation format, you should run the following command: |
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Suggested change
| To convert REFUGE dataset to MMSegmentation format, you should run the following command: | |
| Please run the following command to convert REFUGE dataset: |
MeowZheng
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Thanks for your contribution and we appreciate it a lot. The following instructions would make your pull request more healthy and more easily get feedback. If you do not understand some items, don't worry, just make the pull request and seek help from maintainers.
Motivation
Please describe the motivation of this PR and the goal you want to achieve through this PR.
Modification
In this PR, I finished the code for converting REFUGE data in mmsegmentation format, and add appropriate transforms for training according to a paper for REFUGE segmentation task, and for training, I use unet as backbone, the model config is involved in unet-s5-d16_fcn_4xb4-40k_refuge-256x256.py
BC-breaking (Optional)
Does the modification introduce changes that break the backward-compatibility of the downstream repos?
If so, please describe how it breaks the compatibility and how the downstream projects should modify their code to keep compatibility with this PR.
Use cases (Optional)
If this PR introduces a new feature, it is better to list some use cases here, and update the documentation.
Checklist