{"url":"/dataset/occludedpascal3d","name":"OccludedPASCAL3D+","full_name":null,"description_markdown":"The **OccludedPASCAL3D+** is a dataset is designed to evaluate the robustness to occlusion for a number of computer vision tasks, such as object detection, keypoint detection and pose estimation. In the OccludedPASCAL3D+ dataset, we simulate partial occlusion by superimposing objects cropped from the MS-COCO dataset on top of objects from the PASCAL3D+ dataset. We only use ImageNet subset in PASCAL3D+, which has 10812 testing images.\r\n\r\nSource: [OccludedPASCAL3D+](https://github.com/Angtian/OccludedPASCAL3D)\r\n\r\nImage source: [https://github.com/Angtian/OccludedPASCAL3D](https://github.com/Angtian/OccludedPASCAL3D)","description_withheld":null,"homepage":"https://github.com/Angtian/OccludedPASCAL3D","introduced_date":"2020-05-24","introduced_date_note":null,"introduced_by":{"paper":"/paper/robust-object-detection-under-occlusion-with","title":"Robust Object Detection under Occlusion with Context-Aware CompositionalNets","first_author":"Angtian Wang","url":null},"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["OccludedPASCAL3D+"],"data_loaders":[{"repo":"https://github.com/Angtian/OccludedPASCAL3D","url":"https://github.com/Angtian/OccludedPASCAL3D","frameworks":[]}],"num_papers_in_archive":7,"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."}