{"url":"/dataset/pascal-5i","name":"PASCAL-5i","full_name":null,"description_markdown":"**PASCAL-5i** is a dataset used to evaluate few-shot segmentation. The dataset is sub-divided into 4 folds each containing 5 classes. A fold contains labelled samples from 5 classes that are used for evaluating the few-shot learning method. The rest 15 classes are used for training.\r\n\r\nSource: [AMP: Adaptive Masked Proxies for Few-Shot Segmentation](https://arxiv.org/abs/1902.11123)\r\nImage Source: [https://arxiv.org/pdf/1709.03410.pdf](https://arxiv.org/pdf/1709.03410.pdf)","description_withheld":null,"homepage":"https://github.com/DeepTrial/pascal-5","introduced_date":"2017-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/one-shot-learning-for-semantic-segmentation","title":"One-Shot Learning for Semantic Segmentation","first_author":"Amirreza Shaban","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":"Few-Shot Learning","url":"/task/few-shot-learning","datasets_with_task":"/datasets/task/few-shot-learning"},{"name":"Few-Shot Semantic Segmentation","url":"/task/few-shot-image-segmentation","datasets_with_task":"/datasets/task/few-shot-image-segmentation"}],"languages":[],"variants":["Pascal5i","PASCAL-5i"],"data_loaders":[{"repo":"https://github.com/DeepTrial/pascal-5","url":"https://github.com/DeepTrial/pascal-5","frameworks":[]}],"num_papers_in_archive":177,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/few-shot-semantic-segmentation-on-pascal5i-1","task":"Few-Shot Semantic Segmentation","dataset_variant":"Pascal5i","rows":3,"metrics":["meanIOU"],"first_row_in_archive_order":{"model":"A-MCG-Conv-LSTM","paper":"/paper/attention-based-multi-context-guiding-for-few","metrics":{"meanIOU":"62.2"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/part-aware-prototype-network-for-few-shot","title":"Part-aware Prototype Network for Few-shot Semantic Segmentation","date":"2020-07-13","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/on-the-texture-bias-for-few-shot-cnn","title":"On the Texture Bias for Few-Shot CNN Segmentation","date":"2020-03-09","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/attention-based-multi-context-guiding-for-few","title":"Attention-Based Multi-Context Guiding for Few-Shot Semantic Segmentation","date":"2019-07-01","rows_on_this_dataset":1,"code_links":0,"syntology":null}],"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."}