{"url":"/dataset/deepfish","name":"DeepFish","full_name":null,"description_markdown":"**DeepFish** as a benchmark suite with a large-scale dataset to train and test methods for several computer vision tasks. The dataset consists of approximately 40 thousand images collected underwater from 20 habitats in the marine environments of tropical Australia. It contains classification labels as well as point-level and segmentation labels to have a more comprehensive fish analysis benchmark. These labels enable models to learn to automatically monitor fish count, identify their locations, and estimate their sizes.\r\n\r\nSource: [https://github.com/alzayats/DeepFish](https://github.com/alzayats/DeepFish)\r\nImage Source: [https://github.com/alzayats/DeepFish](https://github.com/alzayats/DeepFish)","description_withheld":null,"homepage":"https://github.com/alzayats/DeepFish","introduced_date":"2020-09-04","introduced_date_note":null,"introduced_by":{"paper":"/paper/a-realistic-fish-habitat-dataset-to-evaluate","title":"A Realistic Fish-Habitat Dataset to Evaluate Algorithms for Underwater Visual Analysis","first_author":"Alzayat Saleh","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Image Classification","url":"/task/image-classification","datasets_with_task":"/datasets/task/image-classification"},{"name":"Semantic Segmentation","url":"/task/semantic-segmentation","datasets_with_task":"/datasets/task/semantic-segmentation"},{"name":"Cross-Domain Few-Shot Object Detection","url":"/task/cross-domain-few-shot-object-detection","datasets_with_task":"/datasets/task/cross-domain-few-shot-object-detection"},{"name":"Fish Detection","url":"/task/fish-detection","datasets_with_task":"/datasets/task/fish-detection"}],"languages":[],"variants":["DeepFish"],"data_loaders":[{"repo":"https://github.com/alzayats/DeepFish","url":"https://github.com/alzayats/DeepFish","frameworks":["pytorch"]}],"num_papers_in_archive":19,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/cross-domain-few-shot-object-detection-on-3","task":"Cross-Domain Few-Shot Object Detection","dataset_variant":"DeepFish","rows":10,"metrics":["mAP"],"first_row_in_archive_order":{"model":"ETS","paper":"/paper/enhance-then-search-an-augmentation-search","metrics":{"mAP":"44.1"},"code_links":[{"title":"jaychempan/ETS","url":"https://github.com/jaychempan/ETS"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/no-time-to-train-training-free-reference","title":"No time to train! Training-Free Reference-Based Instance Segmentation","date":"2025-07-03","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/cdformer-cross-domain-few-shot-object","title":"CDFormer: Cross-Domain Few-Shot Object Detection Transformer Against Feature Confusion","date":"2025-05-02","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/enhance-then-search-an-augmentation-search","title":"Enhance Then Search: An Augmentation-Search Strategy with Foundation Models for Cross-Domain Few-Shot Object Detection","date":"2025-04-06","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/cross-domain-few-shot-object-detection-via","title":"Cross-Domain Few-Shot Object Detection via Enhanced Open-Set Object Detector","date":"2024-02-05","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":11,"samples_ran":9,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/detect-every-thing-with-few-examples","title":"Detect Everything with Few Examples","date":"2023-09-22","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":20,"samples_ran":15,"samples_unverified":5,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/exploring-plain-vision-transformer-backbones","title":"Exploring Plain Vision Transformer Backbones for Object Detection","date":"2022-03-30","rows_on_this_dataset":1,"code_links":11,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":1,"samples_unverified":3,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/detecting-twenty-thousand-classes-using-image","title":"Detecting Twenty-thousand Classes using Image-level Supervision","date":"2022-01-07","rows_on_this_dataset":2,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":3,"samples_harvested":35,"samples_ran":25,"samples_unverified":10,"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."}