Object Detection
ultralytics
PyTorch
detectionbench
computer-vision
underwater
marine-biology
brackish-water
Eval Results (legacy)
Instructions to use dronefreak/brackish-yolo11n with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use dronefreak/brackish-yolo11n with ultralytics:
# Couldn't find a valid YOLO version tag. # Replace XX with the correct version. from ultralytics import YOLOvXX model = YOLOvXX.from_pretrained("dronefreak/brackish-yolo11n") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Add model-index metadata (HF Evaluation Results widget)
Browse files
README.md
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- f1
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base_model: "Ultralytics/YOLO11"
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---
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- f1
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base_model: "Ultralytics/YOLO11"
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model-index:
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- name: YOLOv11n Finetuned on Brackish Underwater
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results:
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- task:
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type: object-detection
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name: Object Detection
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dataset:
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name: Brackish Underwater
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type: brackish
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metrics:
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- type: mAP50
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value: 98.87
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name: mAP@50 (test split)
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- type: mAP50-95
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value: 83.27
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name: mAP@50-95 (test split)
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- type: precision
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value: 98.76
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name: Precision (test split)
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- type: recall
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value: 96.69
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name: Recall (test split)
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source:
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url: https://github.com/dronefreak/DetectionBench
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name: DetectionBench
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---
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