[Attention] OpenMMLab Codecamp

[Harold-lkk]

Introduction

Interested in deeply participating in OpenMMLab projects? Want to learn more about OpenMMLab's awesome tools without wasting plenty of time reading docs? The First OpenMMLab Codecamp has begun!! We provide more than a hundred tasks from seventeenth research directions for you to pick. Whether you are a novice in AI or a senior developer, there are suitable tasks for you to participate in. We will provide quick responses and full guidance to help you smoothly complete those tasks and upgrade to a core contributor of OpenMMLab. We combined Beijing Super Cloud Center to support computing power.

How to participate?

Select the task you are interested in and submit registration here. We will inform you in three days if you have enrolled for the tasks, and then you can formulate the task plan with tutor and start development! Once your PR has passed preliminary review, you can apply for the next task or just wait for the award!

More details: OpenMMLab Activity page。

Here, we listed the tasks in MMOCR, welcome to submit registration!

Tasks

Task	Description	Related skills	Difficulty	Credits
Prepare TextOCR dataset (text detection, text recognition, text spotting) on the DatasetPreparer	MMOCR has introduced Dataset Preparer, allowing one-click data preparation and eliminating the pain of preparing dataset. This task can be divided into four steps: 1. Develop a parser, which is used to parse annotation files. 2. Prepare dataset config: configure the components required for Dataset Preparer. 3. Prepare a metafile, which stores information about the dataset. 4. Prepare sample_anno.md, a single example of the original annotation of the dataset. Dataset Website：TextOCR Tutorial: Dataset Preparer PR example:https://github.com/open-mmlab/mmocr/pull/1514	Python	easy	25
Prepare DeText dataset (text detection, text recognition, text spotting) on the DatasetPreparer	MMOCR has introduced Dataset Preparer, allowing one-click data preparation and eliminating the pain of preparing dataset. This task can be divided into four steps: 1. Develop a parser, which is used to parse annotation files. 2. Prepare dataset config: configure the components required for Dataset Preparer. 3. Prepare a metafile, which stores information about the dataset. 4. Prepare sample_anno.md, a single example of the original annotation of the dataset. Dataset Website：DeText Tutorial: Dataset Preparer PR Example: https://github.com/open-mmlab/mmocr/pull/1514	Python	easy	25
Prepare FUNSD dataset (text detection, text recognition, text spotting) on the DatasetPreparer	MMOCR has introduced Dataset Preparer, allowing one-click data preparation and eliminating the pain of preparing dataset. This task can be divided into four steps: 1. Develop a parser, which is used to parse annotation files. 2. Prepare dataset config: configure the components required for Dataset Preparer. 3. Prepare a metafile, which stores information about the dataset. 4. Prepare sample_anno.md, a single example of the original annotation of the dataset. Dataset Website：FUNSD Tutorial: Dataset Preparer PR Example: https://github.com/open-mmlab/mmocr/pull/1514	Python	easy	25
Prepare BID dataset (text detection, text recognition, text spotting) on the DatasetPreparer	MMOCR has introduced Dataset Preparer, allowing one-click data preparation and eliminating the pain of preparing dataset. This task can be divided into four steps: 1. Develop a parser, which is used to parse annotation files. 2. Prepare dataset config: configure the components required for Dataset Preparer. 3. Prepare a metafile, which stores information about the dataset. 4. Prepare sample_anno.md, a single example of the original annotation of the dataset. Dataset Website：BID Tutorial: Dataset Preparer PR Example: https://github.com/open-mmlab/mmocr/pull/1514	Python	easy	25
Prepare NAF dataset (text detection, text recognition, text spotting) on the DatasetPreparer	MMOCR has introduced Dataset Preparer, allowing one-click data preparation and eliminating the pain of preparing dataset. This task can be divided into four steps: 1. Develop a parser, which is used to parse annotation files. 2. Prepare dataset config: configure the components required for Dataset Preparer. 3. Prepare a metafile, which stores information about the dataset. 4. Prepare sample_anno.md, a single example of the original annotation of the dataset. Dataset Website：NAF Tutorial: Dataset Preparer PR Example: https://github.com/open-mmlab/mmocr/pull/1514	Python	easy	25
SROIE dataset (text detection, text recognition, text spotting) on the DatasetPreparer	MMOCR has introduced Dataset Preparer, allowing one-click data preparation and eliminating the pain of preparing dataset. This task can be divided into four steps: 1. Develop a parser, which is used to parse annotation files. 2. Prepare dataset config: configure the components required for Dataset Preparer. 3. Prepare a metafile, which stores information about the dataset. 4. Prepare sample_anno.md, a single example of the original annotation of the dataset. Dataset Website：SROIE Tutorial: Dataset Preparer PR Example: https://github.com/open-mmlab/mmocr/pull/1514	Python	easy	25
Add missing unit tests	rescale_bbox /rescale_bboxes /equal_len/fpem_ffm	Python	easy	25
Optimize HMeanIOU	HMeanIOU in the case of multi-polygon is slower compared to the official implementation and can be accelerated using multiple thread acceleration or special handling for the case where the output is a rotated rectangle	Python	medium	40
Visualize annotation with images after each data Transform	Visualize annotation with images after each data Transform ，refer to tools/analysis_tools/browse_dataset.py	Python	medium	40

Sign Up Here : application form

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https://discord.gg/KuWMWVbCcD

Discussion group: discord link

Welcome to join the discussion below or in discord. Come to take the challenge, and become a contributor to the OpenMMLab !