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https://github.com/czczup/ViT-Adapter
[ICLR 2023 Spotlight] Vision Transformer Adapter for Dense Predictions
https://github.com/czczup/ViT-Adapter
adapter object-detection semantic-segmentation vision-transformer
Last synced: 4 months ago
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[ICLR 2023 Spotlight] Vision Transformer Adapter for Dense Predictions
- Host: GitHub
- URL: https://github.com/czczup/ViT-Adapter
- Owner: czczup
- License: apache-2.0
- Created: 2022-05-16T17:32:59.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2024-03-18T09:37:37.000Z (11 months ago)
- Last Synced: 2024-10-10T13:42:16.485Z (4 months ago)
- Topics: adapter, object-detection, semantic-segmentation, vision-transformer
- Language: Python
- Homepage: https://arxiv.org/abs/2205.08534
- Size: 1.78 MB
- Stars: 1,229
- Watchers: 17
- Forks: 137
- Open Issues: 72
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
Awesome Lists containing this project
README
# ViT-Adapter
[](https://paperswithcode.com/sota/semantic-segmentation-on-ade20k?p=vision-transformer-adapter-for-dense)
[](https://paperswithcode.com/sota/semantic-segmentation-on-cityscapes?p=vision-transformer-adapter-for-dense)
[](https://paperswithcode.com/sota/semantic-segmentation-on-cityscapes-val?p=vision-transformer-adapter-for-dense)
[](https://paperswithcode.com/sota/semantic-segmentation-on-pascal-context?p=vision-transformer-adapter-for-dense)
[](https://paperswithcode.com/sota/object-detection-on-coco-minival?p=vision-transformer-adapter-for-dense)
[](https://paperswithcode.com/sota/object-detection-on-coco?p=vision-transformer-adapter-for-dense)
[](https://paperswithcode.com/sota/instance-segmentation-on-coco-minival?p=vision-transformer-adapter-for-dense)
[](https://paperswithcode.com/sota/instance-segmentation-on-coco?p=vision-transformer-adapter-for-dense)
[](https://paperswithcode.com/sota/panoptic-segmentation-on-coco-minival?p=vision-transformer-adapter-for-dense)
The official implementation of the paper "[Vision Transformer Adapter for Dense Predictions](https://arxiv.org/abs/2205.08534)".
[Paper](https://arxiv.org/abs/2205.08534) | [Blog in Chinese](https://zhuanlan.zhihu.com/p/608272954) | [Slides](https://drive.google.com/file/d/1LotIZIEnZzKhsANjBTZs3qcezk9fbVCV/view?usp=share_link) | [Poster](https://iclr.cc/media/PosterPDFs/ICLR%202023/12048.png?t=1680764158.7068026) | [Video in English](https://iclr.cc/virtual/2023/poster/12048) | [Video in Chinese](https://www.bilibili.com/video/BV1ry4y1976b/?spm_id_from=333.337.search-card.all.click)
[Segmentation Colab Notebook](https://colab.research.google.com/drive/1yEd5lQMjShloicImtShkwttb74KPGY5U?usp=sharing) | [Detection Colab Notebook](https://colab.research.google.com/drive/1Im7l0dSvEgsP-AJtUOxgbU9a1C3DdSwe?usp=sharing) (thanks [@IamShubhamGupto](https://github.com/IamShubhamGupto), [@dudifrid](https://github.com/dudifrid))
## News
- `2024/01/19`: Train ViT-Adapter with frozen InternViT-6B, see [here](https://github.com/OpenGVLab/InternVL-MMDetSeg)!
- `2023/12/23`: 🚀🚀🚀 We release a ViT-based vision foundation model with 6B parameters, see [here](https://github.com/OpenGVLab/InternVL)!
- `2023/08/31`: 🚀🚀 DINOv2 released the ViT-g-based segmentor with ViT-Adapter, see [here](https://github.com/facebookresearch/dinov2/blob/main/notebooks/semantic_segmentation.ipynb).
- `2023/07/10`: 🚀 Support the weights of [DINOv2](https://github.com/facebookresearch/dinov2) for object detection, see [here](detection/configs/mask_rcnn/dinov2/)!
- `2023/06/26`: ViT-Adapter is adopted by the champion solution [NVOCC](https://opendrivelab.com/e2ead/AD23Challenge/Track_3_NVOCC.pdf) in Track 3 (3D Occupancy Prediction) of the CVPR 2023 Autonomous Driving Challenge.
- `2023/06/07`: ViT-Adapter is used by [ONE-PEACE](https://github.com/OFA-Sys/ONE-PEACE) and they created new SOTA of 63.0 mIoU on ADE20K.
- `2023/04/14`: ViT-Adapter is used in [EVA](https://arxiv.org/abs/2211.07636) and [DINOv2](https://arxiv.org/abs/2304.07193)!
- `2023/01/21`: Our paper is accepted by ICLR 2023!
- `2023/01/17`: We win the champion of [WSDM Cup 2023 Toloka VQA Challenge](/wsdm2023) using ViT-Adapter.
- `2022/10/20`: ViT-Adapter is adopted by Zhang et al. and they ranked 1st in the [UVO Challenge 2022](https://arxiv.org/pdf/2210.09629.pdf).
- `2022/08/22`: ViT-Adapter is adopted by [BEiT-3](https://github.com/microsoft/unilm/tree/master/beit3) and created new SOTA of 62.8 mIoU on ADE20K.
- `2022/06/09`: ViT-Adapter-L achieves 60.4 box AP and 52.5 mask AP on COCO test-dev without Objects365.
- `2022/06/04`: Code and models are released.
- `2022/05/12`: ViT-Adapter-L reaches 85.2 mIoU on Cityscapes test set without coarse data.
- `2022/05/05`: ViT-Adapter-L achieves the SOTA on ADE20K val set with 60.5 mIoU!
## Highlights
- ViT-Adapter supports various dense prediction tasks, including `object detection`, `instance segmentation`, `semantic segmentation`, `visual grounding`, `panoptic segmentation`, etc.
- This codebase includes many SOTA detectors and segmenters to achieve top performance, such as `HTC++`, `Mask2Former`, `DINO`.
https://user-images.githubusercontent.com/23737120/208140362-f2029060-eb16-4280-b85f-074006547a12.mp4
## Abstract
This work investigates a simple yet powerful dense prediction task adapter for Vision Transformer (ViT). Unlike recently advanced variants that incorporate vision-specific inductive biases into their architectures, the plain ViT suffers inferior performance on dense predictions due to weak prior assumptions. To address this
issue, we propose the ViT-Adapter, which allows plain ViT to achieve comparable performance to vision-specific transformers. Specifically, the backbone in our
framework is a plain ViT that can learn powerful representations from large-scale
multi-modal data. When transferring to downstream tasks, a pre-training-free
adapter is used to introduce the image-related inductive biases into the model,
making it suitable for these tasks. We verify ViT-Adapter on multiple dense prediction tasks, including object detection, instance segmentation, and semantic segmentation. Notably, without using extra detection data, our ViT-Adapter-L yields
state-of-the-art 60.9 box AP and 53.0 mask AP on COCO test-dev. We hope that
the ViT-Adapter could serve as an alternative for vision-specific transformers and
facilitate future research. The code and models will be released.
## Method
## Catalog
- [ ] Support flash attention
- [ ] Support faster deformable attention
- [x] Segmentation checkpoints
- [x] Segmentation code
- [x] Detection checkpoints
- [x] Detection code
- [x] Initialization
## Awesome Competition Solutions with ViT-Adapter
**[1st Place Solution for the 5th LSVOS Challenge: Video Instance Segmentation](https://arxiv.org/abs/2306.04091)**
Tao Zhang, Xingye Tian, Yikang Zhou, Yuehua Wu, Shunping Ji, Cilin Yan, Xuebo Wang, Xin Tao, Yuanhui Zhang, Pengfei Wan
[[`Code`](https://github.com/zhang-tao-whu/DVIS)]
[]([https://github.com/zhang-tao-whu/DVIS])
August 28, 2023
**2nd place solution in Scene Understanding for Autonomous Drone Delivery (SUADD'23) competition**
Mykola Lavreniuk, Nivedita Rufus, Unnikrishnan R Nair
[[`Code`](https://github.com/Lavreniuk/2nd-place-solution-in-Scene-Understanding-for-Autonomous-Drone-Delivery)]
[]([https://github.com/Lavreniuk/2nd-place-solution-in-Scene-Understanding-for-Autonomous-Drone-Delivery])
July 18, 2023
**Champion solution in Track 3 (3D Occupancy Prediction) of the CVPR 2023 Autonomous Driving Challenge**
[FB-OCC: 3D Occupancy Prediction based on Forward-Backward View Transformation](https://arxiv.org/abs/2307.01492)
Zhiqi Li, Zhiding Yu, David Austin, Mingsheng Fang, Shiyi Lan, Jan Kautz, Jose M. Alvarez
[[`Code`](https://github.com/NVlabs/FB-BEV)]
[]([https://github.com/NVlabs/FB-BEV])
June 26, 2023
**[3rd Place Solution for PVUW Challenge 2023: Video Panoptic Segmentation](https://arxiv.org/abs/2306.06753)**
Jinming Su, Wangwang Yang, Junfeng Luo, Xiaolin Wei
June 6, 2023
**Champion solution in the Video Scene Parsing in the Wild Challenge at CVPR 2023**
[Semantic Segmentation on VSPW Dataset through Contrastive Loss and Multi-dataset Training Approach](https://arxiv.org/abs/2306.03508)
Min Yan, Qianxiong Ning, Qian Wang
June 3, 2023
**2nd place in the Video Scene Parsing in the Wild Challenge at CVPR 2023**
[Recyclable Semi-supervised Method Based on Multi-model Ensemble for Video Scene Parsing](https://arxiv.org/abs/2306.02894)
Biao Wu, Shaoli Liu, Diankai Zhang, Chengjian Zheng, Si Gao, Xiaofeng Zhang, Ning Wang
June 2, 2023
**[Champion Solution for the WSDM2023 Toloka VQA Challenge](https://arxiv.org/abs/2301.09045)**
Shengyi Gao, Zhe Chen, Guo Chen, Wenhai Wang, Tong Lu
[[`Code`](https://github.com/czczup/ViT-Adapter/tree/main/wsdm2023)]
January 9, 2023
**[1st Place Solutions for the UVO Challenge 2022](https://arxiv.org/abs/2210.09629)**
Jiajun Zhang, Boyu Chen, Zhilong Ji, Jinfeng Bai, Zonghai Hu
October 9, 2022
## Citation
If this work is helpful for your research, please consider citing the following BibTeX entry.
```
@article{chen2022vitadapter,
title={Vision Transformer Adapter for Dense Predictions},
author={Chen, Zhe and Duan, Yuchen and Wang, Wenhai and He, Junjun and Lu, Tong and Dai, Jifeng and Qiao, Yu},
journal={arXiv preprint arXiv:2205.08534},
year={2022}
}
```
## License
This repository is released under the Apache 2.0 license as found in the [LICENSE](LICENSE.md) file.