Update README.md

README.md

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