{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/bamboo-building-mega-scale-vision-dataset","title":"Bamboo: Building Mega-Scale Vision Dataset Continually with Human-Machine Synergy","arxiv_id":"2203.07845","date":"2022-03-15","proceeding":null,"authors":["Yuanhan Zhang","Qinghong Sun","Yichun Zhou","Zexin He","Zhenfei Yin","Kun Wang","Lu Sheng","Yu Qiao","Jing Shao","Ziwei Liu"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2203.07845v2","url_pdf":"https://arxiv.org/pdf/2203.07845v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"bamboo-building-mega-scale-vision-dataset","repo_url":"https://github.com/davidzhangyuanhan/bamboo","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"bamboo-building-mega-scale-vision-dataset","repo_url":"https://github.com/zhangyuanhan-ai/bamboo","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"active-learning","task_name":"Active Learning"},{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"fine-grained-image-classification","task_name":"Fine-Grained Image Classification"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"open-question","task_name":"Open-Ended Question Answering"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[{"slug":"bamboo","name":"Bamboo","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/fine-grained-image-classification-on-caltech","task":"Fine-Grained Image Classification","dataset":"Caltech-101","model":"Bamboo (ViT-B/16)","rank_in_archive_order":17,"of":18,"metrics":{"Accuracy":"94.8"},"uses_additional_data":true},{"leaderboard":"/sota/fine-grained-image-classification-on-oxford-1","task":"Fine-Grained Image Classification","dataset":"Oxford-IIIT Pet Dataset","model":"Bamboo (ViT-B/16)","rank_in_archive_order":8,"of":15,"metrics":{"Accuracy":"95.1%"},"uses_additional_data":true},{"leaderboard":"/sota/fine-grained-image-classification-on-sun397","task":"Fine-Grained Image Classification","dataset":"SUN397","model":"Bamboo (ViT-B/16)","rank_in_archive_order":3,"of":5,"metrics":{"Accuracy":"79.5"},"uses_additional_data":true},{"leaderboard":"/sota/fine-grained-image-classification-on-stanford","task":"Fine-Grained Image Classification","dataset":"Stanford Cars","model":"Bamboo (ViT-B/16)","rank_in_archive_order":58,"of":83,"metrics":{"Accuracy":"93.9%"},"uses_additional_data":true},{"leaderboard":"/sota/image-classification-on-cifar-10","task":"Image Classification","dataset":"CIFAR-10","model":"Bamboo (ViT-B/16)","rank_in_archive_order":53,"of":265,"metrics":{"Percentage correct":"98.2"},"uses_additional_data":false},{"leaderboard":"/sota/image-classification-on-cifar-100","task":"Image Classification","dataset":"CIFAR-100","model":"Bamboo (ViT-B/16)","rank_in_archive_order":25,"of":211,"metrics":{"Percentage correct":"90.2"},"uses_additional_data":true},{"leaderboard":"/sota/image-classification-on-dtd","task":"Image Classification","dataset":"DTD","model":"Bamboo (ViT-B/16)","rank_in_archive_order":4,"of":11,"metrics":{"Accuracy":"81.9"},"uses_additional_data":true},{"leaderboard":"/sota/image-classification-on-flowers-102","task":"Image Classification","dataset":"Flowers-102","model":"Bamboo (ViT-B/16)","rank_in_archive_order":4,"of":52,"metrics":{"Accuracy":"99.7"},"uses_additional_data":true},{"leaderboard":"/sota/image-classification-on-food-101-1","task":"Image Classification","dataset":"Food-101","model":"Bamboo (ViTB/16)","rank_in_archive_order":1,"of":11,"metrics":{"Accuracy (%)":"92.9"},"uses_additional_data":true},{"leaderboard":"/sota/image-classification-on-objectnet","task":"Image Classification","dataset":"ObjectNet","model":"Vit B/16 (Bamboo)","rank_in_archive_order":23,"of":106,"metrics":{"Top-1 Accuracy":"53.9"},"uses_additional_data":true},{"leaderboard":"/sota/image-classification-on-objectnet","task":"Image Classification","dataset":"ObjectNet","model":"ResNet-50 (Bamboo)","rank_in_archive_order":46,"of":106,"metrics":{"Top-1 Accuracy":"38.8"},"uses_additional_data":true},{"leaderboard":"/sota/image-classification-on-omnibenchmark","task":"Image Classification","dataset":"OmniBenchmark","model":"Bamboo-R50","rank_in_archive_order":3,"of":22,"metrics":{"Average Top-1 Accuracy":"45.4"},"uses_additional_data":true}],"syntology":{"syntology_url":"https://syntology.ai/paper/2203.07845","atlas_url":"https://app.syntology.ai/?focus=2203.07845","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.07845"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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