{"url":"/dataset/orbit","name":"ORBIT","full_name":null,"description_markdown":"ORBIT is a real-world few-shot dataset and benchmark grounded in a real-world application of teachable object recognizers for people who are blind/low vision. The dataset contains 3,822 videos of 486 objects recorded by people who are blind/low-vision on their mobile phones, and the benchmark reflects a realistic, highly challenging recognition problem, providing a rich playground to drive research in robustness to few-shot, high-variation conditions.","description_withheld":null,"homepage":"https://city.figshare.com/articles/dataset/ORBIT_A_real-world_few-shot_dataset_for_teachable_object_recognition_collected_from_people_who_are_blind_or_low_vision/14294597","introduced_date":"2021-04-08","introduced_date_note":null,"introduced_by":{"paper":"/paper/orbit-a-real-world-few-shot-dataset-for","title":"ORBIT: A Real-World Few-Shot Dataset for Teachable Object Recognition","first_author":"Daniela Massiceti","url":null},"license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Videos","url":"/datasets/modality/videos"}],"tasks":[{"name":"Few-Shot Image Classification","url":"/task/few-shot-image-classification","datasets_with_task":"/datasets/task/few-shot-image-classification"},{"name":"Few-Shot Learning","url":"/task/few-shot-learning","datasets_with_task":"/datasets/task/few-shot-learning"}],"languages":[],"variants":["ORBIT","ORBIT Clutter Video Evaluation","ORBIT Clean Video Evaluation"],"data_loaders":[],"num_papers_in_archive":7,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/few-shot-image-classification-on-orbit","task":"Few-Shot Image Classification","dataset_variant":"ORBIT Clutter Video Evaluation","rows":3,"metrics":["Frame accuracy"],"first_row_in_archive_order":{"model":"ProtoNetsVideo","paper":"/paper/improving-protonet-for-few-shot-video-object","metrics":{"Frame accuracy":"71.69"},"code_links":[{"title":"guliisgreat/orbit-2022-winner-method","url":"https://github.com/guliisgreat/orbit-2022-winner-method"},{"title":"code-implementation1/Code6","url":"https://github.com/code-implementation1/Code6/tree/main/ProtoNet"},{"title":"MindSpore-paper-code-2/code2","url":"https://github.com/MindSpore-paper-code-2/code2/tree/main/ProtoNet"},{"title":"2023-MindSpore-1/ms-code-215","url":"https://github.com/2023-MindSpore-1/ms-code-215/tree/main/ProtoNet"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/few-shot-image-classification-on-orbit-clean","task":"Few-Shot Image Classification","dataset_variant":"ORBIT Clean Video Evaluation","rows":2,"metrics":["Frame accuracy"],"first_row_in_archive_order":{"model":"SimpleCNAPs + LITE","paper":"/paper/memory-efficient-meta-learning-with-large","metrics":{"Frame accuracy":"82.70"},"code_links":[{"title":"microsoft/ORBIT-Dataset","url":"https://github.com/microsoft/ORBIT-Dataset"},{"title":"cambridge-mlg/LITE","url":"https://github.com/cambridge-mlg/LITE"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/improving-protonet-for-few-shot-video-object","title":"Improving ProtoNet for Few-Shot Video Object Recognition: Winner of ORBIT Challenge 2022","date":"2022-10-01","rows_on_this_dataset":1,"code_links":4,"syntology":null},{"paper":"/paper/memory-efficient-meta-learning-with-large","title":"Memory Efficient Meta-Learning with Large Images","date":"2021-07-02","rows_on_this_dataset":2,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":3,"samples_unverified":6,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/orbit-a-real-world-few-shot-dataset-for","title":"ORBIT: A Real-World Few-Shot Dataset for Teachable Object Recognition","date":"2021-04-08","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":3,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":2,"samples_harvested":12,"samples_ran":6,"samples_unverified":6,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}