---
license: cc-by-4.0
task_categories:
- object-detection
language:
- en
pretty_name: FishDet-M
size_categories:
- 100K<n<1M
---
# FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection

[![arXiv](https://img.shields.io/badge/arXiv-2507.17859-b31b1b.svg)](https://arxiv.org/abs/2507.17859)
[![Project Page](https://img.shields.io/badge/Project-Page-green)](https://lyessaadsaoud.github.io/FishDet-M/)

## Overview

FishDet-M is the largest unified benchmark dataset for fish detection in diverse underwater environments, designed to advance ecological monitoring, aquaculture automation, and marine robotics.

## Key Features

- **📊 Dataset Scale**: 105,556 images with 296,885 annotated fish instances
- **🌊 Diverse Environments**: Marine, brackish, aquarium, and occluded scenes
- **🎯 Unified Format**: COCO-style annotations with bounding boxes
- **🤖 28 Models Evaluated**: Including YOLOv8-v12, DETR, R-CNN variants, and more
- **🧠 CLIP-Based Selection**: Automatic model selection using vision-language alignment

## Dataset Composition

FishDet-M integrates **13 public datasets** spanning:
- Coral reefs and open water
- Aquaculture tanks and ponds
- Clear to highly turbid conditions
- Single fish to dense schools (1-256 instances per image)
- Image resolutions: 78×53 to 4608×3456 pixels

## Performance Highlights

**Top Performing Models:**
- **YOLO12x**: 0.491 mAP (best overall accuracy)
- **YOLO11l**: 0.484 mAP with excellent efficiency
- **YOLOv8n**: 251 FPS for real-time applications

**CLIP-Guided Selector:**
- Dynamic model selection: 0.444 mAP at 80 FPS
- Zero-shot adaptation to diverse scenes

## Key Contributions

1. **Unified Benchmark**: First large-scale dataset harmonizing diverse underwater fish detection datasets
2. **Comprehensive Evaluation**: Systematic comparison of 28 state-of-the-art detection models
3. **Adaptive Selection**: CLIP-based framework for context-aware model deployment
4. **Deployment Guidelines**: Performance insights across occlusion, scale, and visibility conditions

## Applications

- 🐠 Ecological monitoring and biodiversity assessment
- 🎣 Aquaculture automation and fish counting
- 🤖 Underwater robotics and autonomous vehicles
- 🔬 Marine conservation research

## Citation

```bibtex
@article{abujabal2025fishdetm,
  title={FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains},
  author={Abujabal, Muayad and Saad Saoud, Lyes and Hussain, Irfan},
  journal={IEEE Transactions on Image Processing},
  year={2025}
}
```

## Resources

- **Project Website**: [https://lyessaadsaoud.github.io/FishDet-M/](https://lyessaadsaoud.github.io/FishDet-M/)
- **Dataset & Code**: Available through the project page
- **Pretrained Models**: All 28 evaluated models with checkpoints
- **Interactive GUI**: Desktop application for model comparison

## Acknowledgments

This work was supported by the Khalifa University Center for Autonomous Robotic Systems (KUCARS) under Awards RC1-2018-KUCARS and CIRA Awards 8474000419 and 8434000534.

---

*Khalifa University, Abu Dhabi, UAE*