github.com/ZhangYuanhan-AI/Bamboo @main

<a href='https://zhangyuanhan-ai.github.io/' target='_blank'>Yuanhan Zhang</a><sup>1</sup>&emsp;
<a href='https://github.com/Davidzhangyuanhan/Bamboo' target='_blank'>Qinghong Sun</a><sup>2</sup>&emsp;
<a href='https://github.com/Davidzhangyuanhan/Bamboo' target='_blank'>Yichun Zhou</a><sup>3</sup>&emsp;
<a href='https://github.com/Davidzhangyuanhan/Bamboo' target='_blank'>Zexin He</a><sup>3</sup>&emsp;
<a href='https://scholar.google.com.hk/citations?user=ngPR1dIAAAAJ&hl=zh-CN' target='_blank'>Zhenfei Yin</a><sup>4</sup>&emsp;
<a href='https://github.com/Davidzhangyuanhan/Bamboo' target='_blank'>Kun Wang</a><sup>4</sup>&emsp;


<a href='https://lucassheng.github.io/' target='_blank'> Lv Sheng</a><sup>3</sup>&emsp;
<a href='http://mmlab.siat.ac.cn/yuqiao' target='_blank'>Yu Qiao</a><sup>5</sup>&emsp;
<a href='https://amandajshao.github.io/' target='_blank'>Jing Shao</a><sup>4</sup>&emsp;
<a href='https://liuziwei7.github.io/' target='_blank'>Ziwei Liu</a><sup>1</sup>






<sup>1</sup>S-Lab, Nanyang Technological University&emsp;
<sup>2</sup>Beijing University of Posts and Telecommunication&emsp;


<sup>3</sup>Beihang University&emsp;
<sup>4</sup>SenseTime Research&emsp;
<sup>5</sup>Shanghai AI Laboratory

TL;DR

Bamboo is a mega-scale and information-dense dataset for classification and detection pre-training. It is built upon integrating 24 public datasets (e.g. ImagenNet, Places365, Object365, OpenImages) and added new annotations through active learning. Bamboo has 69M image classification annotations (4 times larger than ImageNet) and 32M object bounding boxes (2 times larger than Object365).

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<a href='https://arxiv.org/abs/2203.07845' target='_blank'>[Paper]</a>

Leaderboard

:partying_face:!\ :partying_face:!\ \ \ \ \ \ \

Updates

[11/2022] We release Bamboo-Det. \ [10/2022] We won the first place in Computer Vision in the Wild Challenge(ImageNet-1K in Pre-training track). :partying_face:! \ [06/2022] We split Bamboo-CLS into 30 datasets that represent different realms (e.g. car, mammals, food and etc.) in the natural worlds: HERE \ [06/2022] We release Bamboo-CLS with FC layer, it can classify 115,217 categories. \ [06/2022] We release our label system with many useful attributes!. \ [03/2022] Bamboo-CLS ResNet-50 and Bamboo-CLS ViT B/16 have been released. \ [03/2022] arXiv paper has been released.

About Bamboo

Downloads

• Send your request to yuanhan002@e.ntu.edu.sg. The request should include your name and orgnization as follows. We will notify you by email as soon as possible. NAME: XXX ORGANIZATION: XXX (Bamboo is only for academic research and non-commercial use)

Label sytem

We provide the hierarchy for our label system at HERE. This JSON file includes the following attrubutes of each concept. We hope this information will be beneficial for your research.

We take concept/class dog as an example. - Load JSON file #input with open('PATH-TO-JSON-FILE.json') as f: bamboo = json.load(f) print(bamboo.keys()) #output 'father2child', 'child2father', 'id2name', 'id2desc', 'id2desc_zh', 'id2name_zh' - Check the id (n02084071) of the dog on HERE. - Get the attrubutes you need. - Hypernyms bamboo['child2father']['n02084071']: domestic_animals, canine. - Hyponyms bamboo['father2child']['n02084071']: husky, griffon, shiba inu and etc. - Description bamboo['id2desc']['n02084071']: a member of the genus Canis (probably descended from the common wolf) that has been domesticated by man since prehistoric times; occurs in many breeds. - Included in which public dataset bamboo['id2state']['n02084071']['academic']: openimage, iWildCam2020, STL10, cifar10, iNat2021, ImageNet21K, coco, OpenImage, object365.

Meta File

• Class Name
• Class id -> id
• id -> Class id

Special meta file

Downloading the whole dataset might be unnecessary for most purposes. We provide meta files based on the following dimension. - [ ] Class-wise (e.g. dog, car, boat and etc.) - [x] Superclass-wise (e.g. animal, transportation, structure and etc.): HERE

How to download files from Google drives in the terminal?

• Install gdown pip install gdown
• get the id of the files Link, e.g. https://drive.google.com/file/d/1WEKQ_68Y9i9FzakvPYU6Yj5SOvkZCIEm/view?usp=sharing \ id: 1WEKQ_68Y9i9FzakvPYU6Yj5SOvkZCIEm
• Download gdown https://drive.google.com/uc?id=1WEKQ_68Y9i9FzakvPYU6Yj5SOvkZCIEm

Model Zoo

Bamboo-CLS

Model	Link	Data	cifar10	cifar100	food	pet	flower	sun	stanfordcar	dtd	caltech	fgvc-aircraft	AVG
ResNet-50	Official	CLIP	88.7	70.3	86.4	88.2	96.1	73.3	78.3	76.4	89.6	49.1	79.64
ViT B/16	Official	CLIP	96.2	83.1	92.8	93.1	98.1	78.4	86.7	79.2	94.7	59.5	86.18
ResNet-50	link	Bamboo-CLS	93.6	81.7	85.6	93.0	99.4	71.6	92.3	78.2	93.6	84.4	87.33
ViT B/16	link link_with-FC	Bamboo-CLS	98.5	91.0	93.3	95.3	99.7	79.5	93.9	81.9	94.8	88.8	91.65

Bamboo-DET

Dataset	Model	Link	VOC (AP50)	CITY (MR)	COCO (mmAP)
OpenImages	ResNet-50 + FPN	Official	82.4	16.8	37.4
Object365	ResNet-50 + FPN	Official	86.4	14.7	39.3
Bamboo-DET(Detectron2)	ResNet-50 + FPN	link	87.5	12.6	43.9

Getting Started

Installation

# Create conda environment
conda create -n bamboo python=3.7
conda activate bamboo

# Install Pytorch
conda install pytorch==1.8.0 torchvision==0.9.0 cudatoolkit=10.2 -c pytorch

# Clone and install
git clone https://github.com/Davidzhangyuanhan/Bamboo.git

Linear Probe

Step 1:

Downloading and organizing each downstream dataset as follows

data
├── flowers
│   ├── train/
│   ├── test/
│   ├── train_meta.list
│   ├── test_meta.list

Step 2:

Changing root and meta in Bamboo-Benchmark/configs/100p/config_\*.yaml

Step 3:

Writing the path of the downloaded/your model config in Bamboo-Benchmark/configs/models_cfg/\*.yaml

Step 4:

Writing the name of the downloaded/your model in Bamboo-Benchmark/multi_run_100p.sh

Step 5:

sh Bamboo-Benchmark/multi_run_100p.sh

Citation

If you use this code in your research, please kindly cite the following papers.

@misc{zhang2022bamboo,
      title={Bamboo: Building Mega-Scale Vision Dataset Continually with Human-Machine Synergy}, 
      author={Yuanhan Zhang and Qinghong Sun and Yichun Zhou and Zexin He and Zhenfei Yin and Kun Wang and Lu Sheng and Yu Qiao and Jing Shao and Ziwei Liu},
      year={2022},
      eprint={2203.07845},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Acknowledgement

Thanks to Siyu Chen (https://github.com/Siyu-C) for implementing the Bamboo-Benchmark.