[Attention] OpenMMLab Codecamp 🥇🥈🥉

[zengyh1900]

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 the 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 the 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.

Task	Description	Related skills	Difficulty	Credits
1.x document translation and improvement（1）	review and translate English documents into Chinese version https://github.com/open-mmlab/mmediting/blob/dev-1.x/docs/en/migration/1_overview.md https://github.com/open-mmlab/mmediting/blob/dev-1.x/docs/en/migration/2_runtime.md https://github.com/open-mmlab/mmediting/blob/dev-1.x/docs/en/migration/3_models.md	Python/ PTorch	⭐️⭐️⭐️	10
1.x document translation and improvement（2）	review and translate English documents into Chinese version 4_eval_test.md，https://github.com/open-mmlab/mmediting/blob/dev-1.x/docs/en/migration/4_eval_test.md 5_schedule.md，https://github.com/open-mmlab/mmediting/blob/dev-1.x/docs/en/migration/5_schedule.md 6_data.md，https://github.com/open-mmlab/mmediting/blob/dev-1.x/docs/en/migration/6_data.md	Python/ PTorch	⭐️⭐️⭐️	10
1.x document translation and improvement（3）	review and translate English documents into Chinese version 7_distributed_train.md，https://github.com/open-mmlab/mmediting/blob/dev-1.x/docs/en/migration/7_distributed_train.md 8_optimizers.md， https://github.com/open-mmlab/mmediting/blob/dev-1.x/docs/en/migration/8_optimizers.md 9_visualization.md， https://github.com/open-mmlab/mmediting/blob/dev-1.x/docs/en/migration/9_visualization.md 10_amp.md，https://github.com/open-mmlab/mmediting/blob/dev-1.x/docs/en/migration/10_amp.md	Python/ PTorch	⭐️⭐️⭐️	10
1.x document translation and improvement（4）	review and translate English documents into Chinese version 1_models.md， https://github.com/open-mmlab/mmediting/blob/dev-1.x/docs/en/advanced_guides/1_models.md 2_dataset.md，https://github.com/open-mmlab/mmediting/blob/dev-1.x/docs/en/advanced_guides/2_dataset.md	Python/ PTorch	⭐️⭐️⭐️	10
1.x document translation and improvement（5）	review and translate English documents into Chinese version 3_transforms.md，https://github.com/open-mmlab/mmediting/blob/dev-1.x/docs/en/advanced_guides/3_transforms.md 4_losses.md，https://github.com/open-mmlab/mmediting/blob/dev-1.x/docs/en/advanced_guides/4_losses.md	Python/ PTorch	⭐️⭐️⭐️	10
add type hints	choose one of the parts from the link, and add type hints，more details: #1394	Python/ PTorch	⭐️⭐️⭐️	10
Support Restormer inference	CVPR 2022 super-resolution task, open-source code: https://github.com/swz30/Restormer	Python/ PTorch	⭐️⭐️⭐️⭐️	30
Support SwinIR inference	image super-resolution based on Transformer, open-source code: https://github.com/JingyunLiang/SwinIR	Python/ PTorch	⭐️⭐️⭐️⭐️	30
Support INR-GAN inference	CVPR 2021 image generation, open-source code: https://github.com/universome/inr-gan	Python/ PTorch	⭐️⭐️⭐️⭐️	30
Support DeblurGANv2 inference	ICCV 2019 de-hazing, open-source code: https://github.com/VITA-Group/DeblurGANv2	Python/ PTorch	⭐️⭐️⭐️⭐️	30
Support KID metrics	support KID metric in MMEditing, provide unitest and related documentation	Python/ PTorch	⭐️⭐️⭐️⭐️	30
Support interactive PyQt GUI for image inpainting	Support interactive PyQt GUI for image inpainting and provide related documentation	Python/ PTorch	⭐️⭐️⭐️⭐️	30
Support Style-Based Global Appearance Flow for Virtual Try-On （inference）	CVPR 2022, virtual try-on, open-source code：https://github.com/SenHe/Flow-Style-VTON	Python/ PTorch	⭐️⭐️⭐️⭐️	30
Support CIPS inference	CVPR 2021 image generation, open-source code: https://github.com/saic-mdal/CIPS	Python/ PTorch	⭐️⭐️⭐️⭐️	30
Support PyQt GUI for GAN inversion	Support PyQt GUI for GAN Inversion and provide related documentation	Python/ PTorch	⭐️⭐️⭐️⭐️⭐️	80
Support GRAM inference	CVPR 2022 3D generation, open-source code: https://github.com/microsoft/GRAM	Python/ PTorch	⭐️⭐️⭐️⭐️⭐️	80
Support GMPI inference	ECCV 2022 3D generation, open-source code: https://github.com/apple/ml-gmpi	Python/ PTorch	⭐️⭐️⭐️⭐️⭐️	80
Support GFPGAN inference	CVPR 2021 , paper: https://arxiv.org/abs/2101.04061 open-source code：https://github.com/TencentARC/GFPGAN	Python/ PTorch	⭐️⭐️⭐️⭐️⭐️	80
Support DaGAN （inference）	CVPR 2022 Talking head generation, open-source code: https://github.com/harlanhong/CVPR2022-DaGAN	Python/ PTorch	⭐️⭐️⭐️⭐️⭐️	80
Support GCFSR （inference）	CVPR 2022 Face SR, open-source code: https://github.com/hejingwenhejingwen/GCFSR	Python/ PTorch	⭐️⭐️⭐️⭐️⭐️	80

Sign Up Here : application form
By the way, we strongly encourage you to publish your experience on social media like medium or Twitter with the tag "OpenMMLab Codecamp" to share your experience with more developers!
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 OpenMMLab!