Fish_Dataset_Instance_segmentation Computer Vision Project

This dataset is a slight modification of the already available dataset available in kaggle

The original dataset contains ground truth images of each image, I have converted that it into COCO json format suitable for Faster_RCNN models.

The dataset details: Size - 6000 images Classes - 6 Images_per_class - 1000

(ps:Original dataset contains 9000 images , 9 classes)

Credits:

"A Large Scale Fish Dataset" - Below are the description given by original authors:

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: mehmet.turkan@ieu.edu.tr

Paper : A Large-Scale Dataset for Fish Segmentation and Classification 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".