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https://github.com/ZhangYuanhan-AI/Bamboo
Bamboo: 4 times larger than ImageNet; 2 time larger than Object365; Built by active learning.
https://github.com/ZhangYuanhan-AI/Bamboo
active-learning dataset-generation pre-training
Last synced: about 2 months ago
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Bamboo: 4 times larger than ImageNet; 2 time larger than Object365; Built by active learning.
- Host: GitHub
- URL: https://github.com/ZhangYuanhan-AI/Bamboo
- Owner: ZhangYuanhan-AI
- Created: 2022-03-12T06:43:40.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-04-07T02:55:17.000Z (6 months ago)
- Last Synced: 2024-04-17T00:18:56.556Z (5 months ago)
- Topics: active-learning, dataset-generation, pre-training
- Language: Python
- Homepage:
- Size: 5.41 MB
- Stars: 160
- Watchers: 5
- Forks: 6
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
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
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**).
---
## Leaderboard
[](https://paperswithcode.com/sota/image-classification-on-dtd?p=bamboo-building-mega-scale-vision-dataset) :partying_face:!\
[](https://paperswithcode.com/sota/image-classification-on-food-101-1?p=bamboo-building-mega-scale-vision-dataset) :partying_face:!\
[](https://paperswithcode.com/sota/fine-grained-image-classification-on-sun397?p=bamboo-building-mega-scale-vision-dataset)\
[](https://paperswithcode.com/sota/image-classification-on-flowers-102?p=bamboo-building-mega-scale-vision-dataset)\
[](https://paperswithcode.com/sota/fine-grained-image-classification-on-caltech?p=bamboo-building-mega-scale-vision-dataset)\
[](https://paperswithcode.com/sota/fine-grained-image-classification-on-oxford-1?p=bamboo-building-mega-scale-vision-dataset) \
[](https://paperswithcode.com/sota/image-classification-on-cifar-100?p=bamboo-building-mega-scale-vision-dataset)\
[](https://paperswithcode.com/sota/fine-grained-image-classification-on-stanford?p=bamboo-building-mega-scale-vision-dataset)\
[](https://paperswithcode.com/sota/image-classification-on-cifar-10?p=bamboo-building-mega-scale-vision-dataset)
## Updates
[11/2022] We release [Bamboo-Det](https://entuedu-my.sharepoint.com/:u:/g/personal/yuanhan002_e_ntu_edu_sg/EbkbdpG3MnZKl5oQtcSPI1kBJAqjBQVxdH7F_KpG6UV3Bg?e=5bsd4S). \
[10/2022] We won the **first place** in [Computer Vision in the Wild Challenge(ImageNet-1K in Pre-training track)](https://computer-vision-in-the-wild.github.io/eccv-2022/). :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](./superclass/README.md) \
[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](https://arxiv.org/abs/2203.07845) paper has been **released**.
## About Bamboo
### Downloads
- Send your request to [email protected]. 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](https://drive.google.com/file/d/1x53MYBQvRl9Ii3ahYT6chwAfJ48kMFuy/view?usp=sharing). 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](https://opengvlab.shlab.org.cn/bamboo/search).
- 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](https://drive.google.com/file/d/1qROHNRf9tu8SDUmyL8cIq159ZmvTBY6J/view?usp=share_link)
- [Class id -> id](https://drive.google.com/file/d/1s8lytaOVU7GrvGTSs8028rAlILXENSs3/view?usp=share_link)
- [id -> Class id](https://drive.google.com/file/d/164DiLaMX2iN9PwfZqmXF9UitOwEegcGW/view?usp=share_link)
### 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](./superclass/README.md)
### 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](https://drive.google.com/file/d/1DrNT5gTK5ouB9c4VMzYpPuAFt-GWA9z3/view?usp=sharing) | 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](https://drive.google.com/file/d/1JNyx81QfB5Fkrho6tBCFqoUYI-VLEvX6/view?usp=sharing) [link_with-FC](https://drive.google.com/file/d/1JNyx81QfB5Fkrho6tBCFqoUYI-VLEvX6/view?usp=sharing) | 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](https://github.com/facebookresearch/detectron2))|ResNet-50 + FPN| [link](https://entuedu-my.sharepoint.com/:u:/g/personal/yuanhan002_e_ntu_edu_sg/EbkbdpG3MnZKl5oQtcSPI1kBJAqjBQVxdH7F_KpG6UV3Bg?e=5bsd4S)| 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.
[](https://hits.seeyoufarm.com)