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--- |
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language: en |
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license: agpl-3.0 |
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tags: |
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- yolov11 |
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- object-detection |
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- computer-vision |
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- yolo |
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- vessel |
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- boat |
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- ship |
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- satellite |
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- remote-sensing |
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- google-earth |
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task: object-detection |
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library: ultralytics |
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base_model: |
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- Ultralytics/YOLO11 |
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--- |
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# Vessel Detection Model |
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This model performs vessel detection using YOLOv11 architecture. Two versions are available: |
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- YOLOv11-nano (public) |
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- YOLOv11-xlarge (private, enterprise) |
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## Model Description |
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The model is trained to detect vessels in maritime imagery. It uses the YOLOv11 architecture with improvements in detection accuracy and speed. |
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## Performance |
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**YOLOv11-nano (Public Version)** |
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- mAP50: 0.474 |
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- mAP50-95: 0.192 |
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- Precision: 0.587 |
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- Recall: 0.476 |
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**YOLOv11-xlarge (Enterprise Version)** |
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- mAP50: 0.579 |
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- mAP50-95: 0.318 |
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- Precision: 0.612 |
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- Recall: 0.571 |
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For access to the enterprise version, please contact [email protected] |
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## Usage |
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```bash |
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pip install huggingface_hub ultralytics matplotlib |
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``` |
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```python |
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from huggingface_hub import hf_hub_download |
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from ultralytics import YOLO |
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import matplotlib.pyplot as plt |
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# Download the model file and a sample image from Hugging Face |
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repo_id = "truthdotphd/vessel-detection" |
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model_path = hf_hub_download(repo_id=repo_id, filename="model.pt") |
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image_path = hf_hub_download(repo_id=repo_id, filename="vessels.jpg") |
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# Load the model |
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model = YOLO(model_path) |
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# Use the model for inference |
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results = model(image_path) |
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plt.figure(figsize=(10, 10)) |
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plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) # Convert BGR to RGB for matplotlib |
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plt.axis('off') |
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plt.show() |
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``` |
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## Limitations |
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- Performance may vary depending on image quality and lighting conditions |
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- Optimized for daytime maritime imagery |
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- Detection accuracy may decrease in crowded scenes |
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## Training Data |
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The model was trained on a proprietary dataset of maritime vessel images. The dataset includes various vessel types under different weather and lighting conditions. |
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## Training Procedure |
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- Architecture: YOLOv11 |
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- Training Framework: Ultralytics |
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- Hardware: NVIDIA GPUs |
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- Optimization: AdamW optimizer |
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For enterprise solutions and access to YOLOv11-xlarge, please contact [email protected] |
