原文：

A Large Scale Fish Dataset

A Large-Scale Dataset for Fish Segmentation and Classification

This dataset contains 9 different seafood types collected from a supermarket in Izmir, Turkey for a university-industry collaboration project at Izmir University of Economics, and this work was published in ASYU 2020. Dataset includes, gilt head bream, red sea bream, sea bass, red mullet, horse mackerel, black sea sprat, striped red mullet, trout, shrimp image samples.

This dataset was collected in order to carry out segmentation, feature extraction and classification tasks and compare the common segmentation, feature extraction and classification algortihms (Semantic Segmentation, Convolutional Neural Networks, Bag of Features). All of the experiment results prove the usability of our dataset for purposes mentioned above.

Images were collected via 2 different cameras, Kodak Easyshare Z650 and Samsung ST60. Therefore, the resolution of the images are 2832 x 2128, 1024 x 768, respectively. Before the segmentation, feature extraction and classification process, the dataset was resized to 590 x 445 by preserving the aspect ratio. After resizing the images, all labels in the dataset were augmented (by flipping and rotating). At the end of the augmentation process, the number of total images for each class became 2000; 1000 for the RGB fish images and 1000 for their pair-wise ground truth labels.

The dataset contains 9 different seafood types. For each class, there are 1000 augmented images and their pair-waise augmented ground truths. Each 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". For 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".

譯文：

大型魚類數據集

用于魚類分割和分類的大規模數據集

該數據集包含9種不同的海鮮類型收集從超市在土耳其伊茲密爾，為一個大學工業合作項目在伊茲密爾經濟大學，這項工作發表在ASU 2020。數據集包括：金頭鯛、紅海鯛、鱸魚、紅鯔魚、馬鯖魚、黑海鯡魚、條紋紅鯔魚、鱒魚、蝦圖像樣本。

收集該數據集是為了執行分割、特征提取和分類任務，并比較常用的分割、特征提取和分類算法（語義分割、卷積神經網絡、特征包）。所有的實驗結果都證明了我們的數據集對于上述目的的可用性。

圖像是通過柯達易共享Z650和三星ST60兩種不同的相機采集的。因此，28321024個圖像的分辨率分別為283276x。在分割、特征提取和分類過程之前，通過保留縱橫比將數據集的大小調整為590 x 445。在調整圖像大小后，數據集中的所有標簽都被增加（通過翻轉和旋轉）。在增強過程結束時，每個類的總圖像數為2000；1000個用于RGB魚類圖像，1000個用于它們的成對地面真相標簽。

該數據集包含9種不同的海鮮類型。每門課都有1000張增強圖像和一對增強的地面真相。每個類都可以在“Fish_數據集”文件中找到，其中包含它們的基本真相標簽。每個類的所有圖像都從“00000.png”到“01000.png”排序。例如，如果要訪問數據集中蝦的地面真實圖像，應遵循的順序是“Fish->Shrimp->Shrimp GT”。

大家可以到官網地址下載數據集，我自己也在百度網盤分享了一份。可關注本人公眾號，回復“202203”獲取下載鏈接。

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