{"url":"/dataset/isod","name":"ISOD","full_name":"Indoor Small Object Dataset","description_markdown":"ISOD contains **2,000 manually labelled RGB-D images** from **20 diverse sites**, each featuring **over 30 types of small objects** randomly placed amidst the items already present in the scenes. These objects, **typically ≤3cm in height**, include LEGO blocks, rags, slippers, gloves, shoes, cables, crayons, chalk, glasses, smartphones (and their cases), fake banana peels, fake pet waste, and piles of toilet paper, among others. These items were chosen because they either threaten the safe operation of indoor mobile robots or create messes if run over. \r\n\r\nIn addition to **RGB** images, ISOD also includes corresponding **depth** images and **IMU** readings. A reference image of each floor type was also recorded using a smartphone. \r\n\r\nThis dataset was used as a real-world validation dataset in the original work to explore the performance of the model beyond synthetic data, specifically focusing on the potential application of real-time robot navigation.","description_withheld":null,"homepage":"https://www.kaggle.com/datasets/yuchen66/indoor-small-object-dataset","introduced_date":"2023-06-15","introduced_date_note":null,"introduced_by":{"paper":"/paper/a-self-supervised-miniature-one-shot-texture","title":"A Self-Supervised Miniature One-Shot Texture Segmentation (MOSTS) Model for Real-Time Robot Navigation and Embedded Applications","first_author":"Yu Chen","url":null},"license":{"name":"CC BY-NC-SA","url":"https://paperswithcode.com/datasets/license"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Time series","url":"/datasets/modality/time-series"}],"tasks":[{"name":"2D Semantic Segmentation","url":"/task/2d-semantic-segmentation","datasets_with_task":"/datasets/task/2d-semantic-segmentation"},{"name":"Robot Navigation","url":"/task/robot-navigation","datasets_with_task":"/datasets/task/robot-navigation"},{"name":"Object Detection In Indoor Scenes","url":"/task/object-detection-in-indoor-scenes","datasets_with_task":"/datasets/task/object-detection-in-indoor-scenes"},{"name":"Indoor Monocular Depth Estimation","url":"/task/indoor-monocular-depth-estimation","datasets_with_task":"/datasets/task/indoor-monocular-depth-estimation"},{"name":"Texture Classification","url":"/task/texture-classification","datasets_with_task":"/datasets/task/texture-classification"},{"name":"Texture Image Retrieval","url":"/task/texture-image-retrieval-1","datasets_with_task":"/datasets/task/texture-image-retrieval-1"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["ISOD"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"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."}