---
license: cc-by-4.0
---
# STAFDD Dataset

## Overview
The **STAFDD Dataset** is a fish disease detection dataset designed for training and evaluating deep learning models under aquaculture scenarios.  
It supports **object detection**, **spatio-temporal analysis**, and **video-based disease assessment**, and is used in the paper:

> **STAFDD: A Spatio-Temporal Automatic Fish Disease Detection Method**

The dataset is organized in **YOLO format** and includes:
- Annotated images for YOLO-based training
- Pretrained `.pt` model weights
- Raw test videos for inference and evaluation

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## Dataset Structure
The dataset is organized as follows:

STAFDD-dataset/
├── images/
│ ├── train/
│ ├── val/
│ └── test/
├── labels/
│ ├── train/
│ ├── val/
│ └── test/
├── videos/
│ └── test_videos/
├── models/
│ └── ReID.pt
├── data.yaml
└── README.md


- `images/`: RGB images extracted from aquaculture videos  
- `labels/`: YOLO-format annotations (`.txt`)  
- `videos/`: Raw test videos used for model evaluation  
- `models/`: Pretrained model weights (`.pt`)  
- `data.yaml`: YOLO training configuration file

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## Annotation Format
Annotations follow the **YOLO object detection format**:

<class_id> <x_center> <y_center> <width> <height>


- Coordinates are normalized to `[0, 1]`
- One annotation file per image
- Bounding boxes correspond to visible disease-related regions on fish bodies

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## Classes
The dataset focuses on **fish disease-related visual symptoms**.  
Class definitions are consistent with those described in the associated paper.

> ⚠️ Note: Class semantics should be interpreted together with the experimental section of the paper.

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## Data Splits
The dataset is divided into three subsets:

- **Training set**
- **Validation set**
- **Test set**

To reduce data leakage, frame sampling and dataset splitting are performed with temporal consistency considerations, avoiding random shuffling of adjacent video frames.

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## Intended Use
This dataset is intended for:
- Fish disease detection
- Small object detection in aquaculture environments
- Video-based disease analysis
- Research on spatio-temporal fish health monitoring

It is suitable for training YOLO-based detectors and evaluating models on real-world aquaculture videos.

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## License
This dataset is released under the **CC BY 4.0** license.

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## Citation
If you use this dataset, please cite the following paper:

```bibtex
@article{wang2024stafdd,
  title={STAFDD: A Spatio-Temporal Automatic Fish Disease Detection Method},
  author={Wang, Bo and others},
  journal={},
  year={2024}
}


Contact

For questions or collaborations, please contact:

Bo Wang
Email: 3020201781@jsnu.edu