{"url":"/dataset/idd","name":"IDD","full_name":"Indian Driving Dataset","description_markdown":"IDD is a dataset for road scene understanding in unstructured environments used for semantic segmentation and object detection for autonomous driving. It consists of 10,004 images, finely annotated with 34 classes collected from 182 drive sequences on Indian roads. \r\n\r\nSource: [IDD: A Dataset for Exploring Problems of Autonomous Navigation in Unconstrained Environments](/paper/idd-a-dataset-for-exploring-problems-of)\r\nImage Source: [Varma et al](https://arxiv.org/pdf/1811.10200v1.pdf)","description_withheld":null,"homepage":"http://idd.insaan.iiit.ac.in/","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/idd-a-dataset-for-exploring-problems-of","title":"IDD: A Dataset for Exploring Problems of Autonomous Navigation in Unconstrained Environments","first_author":"Girish Varma","url":null},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Semantic Segmentation","url":"/task/semantic-segmentation","datasets_with_task":"/datasets/task/semantic-segmentation"},{"name":"Domain Adaptation","url":"/task/domain-adaptation","datasets_with_task":"/datasets/task/domain-adaptation"},{"name":"Autonomous Driving","url":"/task/autonomous-driving","datasets_with_task":"/datasets/task/autonomous-driving"}],"languages":[],"variants":["IDD"],"data_loaders":[],"num_papers_in_archive":98,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/panoptic-segmentation-on-indian-driving-1","task":"Panoptic Segmentation","dataset_variant":"Indian Driving Dataset","rows":4,"metrics":["PQ"],"first_row_in_archive_order":{"model":"EfficientPS","paper":"/paper/efficientps-efficient-panoptic-segmentation","metrics":{"PQ":"51.1"},"code_links":[{"title":"DeepSceneSeg/EfficientPS","url":"https://github.com/DeepSceneSeg/EfficientPS"},{"title":"vincrichard/EfficientPS","url":"https://github.com/vincrichard/EfficientPS"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/efficientps-efficient-panoptic-segmentation","title":"EfficientPS: Efficient Panoptic Segmentation","date":"2020-04-05","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/seamless-scene-segmentation","title":"Seamless Scene Segmentation","date":"2019-05-03","rows_on_this_dataset":1,"code_links":5,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":1,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/upsnet-a-unified-panoptic-segmentation","title":"UPSNet: A Unified Panoptic Segmentation Network","date":"2019-01-12","rows_on_this_dataset":1,"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":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/panoptic-feature-pyramid-networks","title":"Panoptic Feature Pyramid Networks","date":"2019-01-08","rows_on_this_dataset":1,"code_links":12,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":12,"samples_ran":7,"samples_unverified":5,"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":3,"samples_harvested":17,"samples_ran":11,"samples_unverified":6,"pointer_only_for_licence":3,"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."}