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🚀 Excited to open-source the Brackish Underwater Object Detection Model Zoo on Hugging Face.
This release includes:
- 🤖 8 YOLOv8, YOLOv11, and YOLOv26 object detection models trained on Brackish, spanning nano through x-large variants (RF-DETR variants to follow once I have the compute for them).
- 🐟 Benchmarked on Brackish's underwater marine-animal detection task — crab, fish, jellyfish, shrimp, small_fish, and starfish, captured 9 meters below the surface under naturally varying visibility conditions.
- 📊 Detailed model cards with mAP/precision/recall, per-class breakdowns, PR/F1 curves and confusion matrices, qualitative detection showcases, and full training configurations for reproducibility.
Headline numbers:
- 🏆 Best mAP@50: 99.3% (YOLOv8s), 85.65% mAP@50:95, 99.36% precision.
- ⚡ Best efficiency tradeoff: YOLOv26n hits 98.95% mAP@50 at just 6.1 GFLOPs (2.6M params) — within 0.35 points of the top model while using ~4.7x fewer FLOPs (28.6 GFLOPs). The whole zoo actually clusters tightly here (98.74–99.3% mAP@50 across all 8 models), so there's little accuracy left on the table by going small on this dataset.
If you're working on underwater perception, marine biology/ecology monitoring, or robotics in low-visibility environments, I hope these resources are useful.
📦 Dataset:
dronefreak/Brackish
🤖 Model Collection: dronefreak/brackish-underwater-object-detection-model-zoo-6a7c153687b2821da0c6591d
Feedback, bug reports, and contributions are always welcome.
This release includes:
- 🤖 8 YOLOv8, YOLOv11, and YOLOv26 object detection models trained on Brackish, spanning nano through x-large variants (RF-DETR variants to follow once I have the compute for them).
- 🐟 Benchmarked on Brackish's underwater marine-animal detection task — crab, fish, jellyfish, shrimp, small_fish, and starfish, captured 9 meters below the surface under naturally varying visibility conditions.
- 📊 Detailed model cards with mAP/precision/recall, per-class breakdowns, PR/F1 curves and confusion matrices, qualitative detection showcases, and full training configurations for reproducibility.
Headline numbers:
- 🏆 Best mAP@50: 99.3% (YOLOv8s), 85.65% mAP@50:95, 99.36% precision.
- ⚡ Best efficiency tradeoff: YOLOv26n hits 98.95% mAP@50 at just 6.1 GFLOPs (2.6M params) — within 0.35 points of the top model while using ~4.7x fewer FLOPs (28.6 GFLOPs). The whole zoo actually clusters tightly here (98.74–99.3% mAP@50 across all 8 models), so there's little accuracy left on the table by going small on this dataset.
If you're working on underwater perception, marine biology/ecology monitoring, or robotics in low-visibility environments, I hope these resources are useful.
📦 Dataset:
dronefreak/Brackish
🤖 Model Collection: dronefreak/brackish-underwater-object-detection-model-zoo-6a7c153687b2821da0c6591d
Feedback, bug reports, and contributions are always welcome.
