Upload README.md

README.md

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-license: cc-by-4.0
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+# 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 and segmentation masks
+- **🤖 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*