Bamboo: Building Mega-Scale Vision Dataset Continually with Human-Machine Synergy
Large-scale datasets play a vital role in computer vision. But current datasets are annotated blindly without differentiation to samples, making the data collection inefficient and unscalable. The open question is how to build a mega-scale dataset actively. Although advanced active learning algorithms might be the answer, we experimentally found that they are lame in the realistic annotation scenario where out-of-distribution data is extensive. This work thus proposes a novel active learning framework for realistic dataset annotation. Equipped with this framework, we build a high-quality vision dataset -- Bamboo, which consists of 69M image classification annotations with 119K categories and 28M object bounding box annotations with 809 categories. We organize these categories by a hierarchical taxonomy integrated from several knowledge bases. The classification annotations are four times larger than ImageNet22K, and that of detection is three times larger than Object365. Compared to ImageNet22K and Objects365, models pre-trained on Bamboo achieve superior performance among various downstream tasks (6.2% gains on classification and 2.1% gains on detection). We believe our active learning framework and Bamboo are essential for future work.
PDF Abstract
Datasets
Introduced in the Paper:
Bamboo
Used in the Paper:
CIFAR-10
ImageNet
MS COCO
CIFAR-100
Oxford 102 Flower
Places
ImageNet-1K
Stanford Cars
DTD
Food-101
Caltech-101
iNaturalist
Objects365
ObjectNet
JFT-300M
CityPersons
Oxford-IIIT Pet Dataset
CompCars
Oxford-IIIT Pets
SUN397
OmniBenchmark
FoodX-251
Results from the Paper
Ranked #1 on
Image Classification
on Food-101
(using extra training data)
