Papers › Bamboo: Building Mega-Scale Vision Dataset Continually with Human-Machine Synergy

Bamboo: Building Mega-Scale Vision Dataset Continually with Human-Machine Synergy

15 Mar 2022arXiv:2203.07845archive 2025-07-28

Yuanhan Zhang, Qinghong Sun, Yichun Zhou, Zexin He, Zhenfei Yin, Kun Wang, Lu Sheng, Yu Qiao, Jing Shao, Ziwei Liu

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.

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Tasks

Active LearningClassificationFine-Grained Image ClassificationImage ClassificationObject DetectionOpen-Ended Question Answeringimage-classification

Datasets

Introduced by this paper, per the archive.

Bamboo

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Fine-Grained Image Classification Caltech-101 Bamboo (ViT-B/16) Accuracy 94.8 #17 of 18 Archive leaderboard report
Fine-Grained Image Classification Oxford-IIIT Pet Dataset Bamboo (ViT-B/16) Accuracy 95.1% #8 of 15 Archive leaderboard report
Fine-Grained Image Classification SUN397 Bamboo (ViT-B/16) Accuracy 79.5 #3 of 5 Archive leaderboard report
Fine-Grained Image Classification Stanford Cars Bamboo (ViT-B/16) Accuracy 93.9% #58 of 83 Archive leaderboard report
Image Classification CIFAR-10 Bamboo (ViT-B/16) Percentage correct 98.2 #53 of 265 Archive leaderboard report
Image Classification CIFAR-100 Bamboo (ViT-B/16) Percentage correct 90.2 #25 of 211 Archive leaderboard report
Image Classification DTD Bamboo (ViT-B/16) Accuracy 81.9 #4 of 11 Archive leaderboard report
Image Classification Flowers-102 Bamboo (ViT-B/16) Accuracy 99.7 #4 of 52 Archive leaderboard report
Image Classification Food-101 Bamboo (ViTB/16) Accuracy (%) 92.9 #1 of 11 Archive leaderboard report
Image Classification ObjectNet Vit B/16 (Bamboo) Top-1 Accuracy 53.9 #23 of 106 Archive leaderboard report
Image Classification ObjectNet ResNet-50 (Bamboo) Top-1 Accuracy 38.8 #46 of 106 Archive leaderboard report
Image Classification OmniBenchmark Bamboo-R50 Average Top-1 Accuracy 45.4 #3 of 22 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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