/Bamboo

Bamboo: 4 times larger than ImageNet; 2 time larger than Object365; Built by active learning.

Primary LanguagePython

fig1

Yuanhan Zhang1  Qinghong Sun2  Yichun Zhou3  Zexin He3  Zhenfei Yin4  Kun Wang4 
Lv Sheng3  Yu Qiao5  Jing Shao4  Ziwei Liu1
1S-Lab, Nanyang Technological University  2Beijing University of Posts and Telecommunication 
3Beihang University  4SenseTime Research  5Shanghai AI Laboratory

Pineapple

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).

[Paper]

Leaderboard

PWC 🥳!
PWC 🥳!
PWC
PWC
PWC
PWC
PWC
PWC
PWC

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). 🥳!
[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.

Pineapple

Meta File

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.)
  • Superclass-wise (e.g. animal, transportation, structure and etc.): HERE

How to download files from Google drives in the terminal?

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.

Hits