| | --- |
| | license: cc-by-4.0 |
| | language: |
| | - en |
| | --- |
| | |
| | > [!Note] |
| | > This is a copy version from [A large scale fish dataset in Kaggle](https://www.kaggle.com/datasets/crowww/a-large-scale-fish-dataset). And if you download this, you should follow the same license as original(CC-BY-NC4.0) and cite it as original readme says. |
| | > |
| | > Original Dataset Authors : O. Ulucan, D. Karakaya, M. Turkan |
| | > |
| | > For eaiser use, the dataset has been formated to 5 columns : `image_id`, `image`, `mask`, `class_id`, `class_name`, which is easier for you to load in hugging face and use. |
| | |
| | |
| | ***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 |
| | |
| | |
| | ***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. |
| | 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 algortihms (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 data in this data set*** |
| | |
| | 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". |
