{"url":"/dataset/a-large-scale-fish-dataset","name":"A Large Scale Fish Dataset","full_name":"A Large-Scale Dataset for Fish Segmentation and Classification","description_markdown":"This dataset contains 9 different seafood types collected from a supermarket in Izmir, Turkey\r\nfor a university-industry collaboration project at Izmir University of Economics, and this work\r\nwas published in ASYU 2020.\r\nThe dataset includes gilt head bream, red sea bream, sea bass, red mullet, horse mackerel,\r\nblack sea sprat, striped red mullet, trout, shrimp image samples.\r\n\r\nIf you use this dataset in your work, please consider to cite:\r\n\r\n@inproceedings{ulucan2020large,\r\ntitle={A Large-Scale Dataset for Fish Segmentation and Classification},\r\nauthor={Ulucan, Oguzhan and Karakaya, Diclehan and Turkan, Mehmet},\r\nbooktitle={2020 Innovations in Intelligent Systems and Applications Conference (ASYU)},\r\npages={1--5},\r\nyear={2020},\r\norganization={IEEE}\r\n}\r\n\r\nThe dataset contains 9 different seafood types. For each class, there are 1000 augmented images and their pair-wise augmented ground truths.\r\nEach class can be found in the \"Fish_Dataset\" file with their ground truth labels. All images for each class are ordered from \"00000.png\" to \"01000.png\".\r\nFor example, if you want to access the ground truth images of the shrimp in the dataset, the order should be followed is \"Fish->Shrimp->Shrimp GT\". \r\n\r\nThis dataset was collected in order to carry out segmentation, feature extraction, and classification tasks\r\nand compare the common segmentation, feature extraction, and classification algorithms (Semantic Segmentation, Convolutional Neural Networks, Bag of Features).\r\nAll of the experiment results prove the usability of our dataset for purposes mentioned above.","description_withheld":null,"homepage":"https://ieeexplore.ieee.org/abstract/document/9259867","introduced_date":"2020-11-23","introduced_date_note":null,"introduced_by":null,"license":{"name":"Attribution 4.0 International (CC BY 4.0)","url":"https://creativecommons.org/licenses/by/4.0/"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"2D Object Detection","url":"/task/2d-object-detection","datasets_with_task":"/datasets/task/2d-object-detection"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["A Large Scale Fish Dataset"],"data_loaders":[],"num_papers_in_archive":1,"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."}