{"url":"/dataset/wld","name":"WLD","full_name":"WildLife Documentary","description_markdown":"**WildLife Documentary** is an animal object detection dataset. It contains 15 documentary films that are downloaded from YouTube. The videos vary between 9 minutes to as long as 50 minutes, with resolution ranging from 360p\r\nto 1080p. A unique property of this dataset is that all videos are accompanied with subtitles that are automatically generated from speech by YouTube. The subtitles are revised manually to correct obvious spelling mistakes. All the animals in the videos are annotated, resulting in more than 4098 object tracklets of 60 different visual\r\nconcepts, e.g., ‘tiger’, ‘koala’, ‘langur’, and ‘ostrich’.","description_withheld":null,"homepage":"https://github.com/hellock/WLD","introduced_date":"2017-07-30","introduced_date_note":null,"introduced_by":{"paper":"/paper/discover-and-learn-new-objects-from","title":"Discover and Learn New Objects from Documentaries","first_author":"Kai Chen","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"General Reinforcement Learning","url":"/task/general-reinforcement-learning","datasets_with_task":"/datasets/task/general-reinforcement-learning"}],"languages":[],"variants":["WLD"],"data_loaders":[{"repo":"https://github.com/hellock/WLD","url":"https://github.com/hellock/WLD","frameworks":[]}],"num_papers_in_archive":3,"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."}