A Large Scale Fish Dataset

***A Large-Scale Dataset for Segmentation and Classification***

Authors: O. Ulucan, D. Karakaya, M. Turkan
Department of Electrical and Electronics Engineering, Izmir University of Economics, Izmir, Turkey
Corresponding author: M. Turkan
Contact Information: [e-mail]

- Paper : [A Large-Scale Dataset for Fish Segmentation and Classification](https://ieeexplore.ieee.org/abstract/document/9259867)

***General Introduction***

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.
The dataset includes gilt head bream, red sea bream, sea bass, red mullet, horse mackerel,
black sea sprat, striped red mullet, trout, shrimp image samples.

If you use this dataset in your work, please consider to cite:

@inproceedings{ulucan2020large,
title={A Large-Scale Dataset for Fish Segmentation and Classification},
author={Ulucan, Oguzhan and Karakaya, Diclehan and Turkan, Mehmet},
booktitle={2020 Innovations in Intelligent Systems and Applications Conference (ASYU)},
pages={1--5},
year={2020},
organization={IEEE}
}

* O.Ulucan, D.Karakaya, and M.Turkan.(2020) A large-scale dataset for fish segmentation and classification.
In Conf. Innovations Intell. Syst. Appli. (ASYU)

***Purpose of the work***

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

***Data Gathering Equipment and Data Augmentation***

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.

***Description of the dataset***

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

《大规模鱼类分割与分类数据集》

作者：O. Ulucan, D. Karakaya, M. Turkan
所属部门：土耳其伊兹密尔经济大学电气与电子工程系，伊兹密尔，土耳其
通讯作者：M. Turkan
联系方式：[e-mail]

- 论文：[A Large-Scale Dataset for Fish Segmentation and Classification](https://ieeexplore.ieee.org/abstract/document/9259867)

***总体介绍***

本数据集包含从土耳其伊兹密尔的一家超市收集的9种不同海鲜样本，旨在伊兹密尔经济大学大学-产业合作项目中应用，相关研究成果已发表于2020年ASYU会议。
数据集涵盖了金头鲈、红海鲈鱼、海鲈鱼、红鲷鱼、马鲛鱼、黑海鳀鱼、条斑红鲷鱼、鲑鱼和虾的图像样本。

若您在研究中使用此数据集，请考虑引用以下文献：

@inproceedings{ulucan2020large,
title={A Large-Scale Dataset for Fish Segmentation and Classification},
author={Ulucan, Oguzhan and Karakaya, Diclehan and Turkan, Mehmet},
booktitle={2020 Innovations in Intelligent Systems and Applications Conference (ASYU)},
pages={1--5},
year={2020},
organization={IEEE}
}

* O.Ulucan, D.Karakaya, and M.Turkan.(2020) A large-scale dataset for fish segmentation and classification.
In Conf. Innovations Intell. Syst. Appli. (ASYU)

***研究目的***

本数据集的收集旨在执行分割、特征提取和分类任务，并比较常见的分割、特征提取和分类算法（语义分割、卷积神经网络、特征包）。所有实验结果均证实了本数据集在上述目的上的可用性。

***数据收集设备与数据增强***

图像通过两台不同的相机收集，分别为Kodak Easyshare Z650和Samsung ST60。因此，图像的分辨率分别为2832 x 2128和1024 x 768。
在分割、特征提取和分类过程之前，数据集被调整为590 x 445大小，同时保持图像的宽高比。调整图像大小后，数据集中的所有标签都进行了增强（包括翻转和旋转）。
在增强过程结束后，每个类别的总图像数量达到2000张；RGB鱼类图像1000张，其成对地面真实标签也1000张。

***数据集描述***

数据集包含9种不同的海鲜类型。对于每个类别，均有1000张增强图像及其成对增强地面真实标签。
每个类别的图像及其地面真实标签均可在“Fish_Dataset”文件中找到。每个类别的所有图像按“00000.png”至“01000.png”的顺序排列。

例如，如果您想访问数据集中的虾的地面真实图像，应遵循的顺序是“Fish->Shrimp->Shrimp GT”。