  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.
