Yolo11x Sea Urchin Detector

Evaluation of YOLOv11n & YOLOv11x Performance

results

• link to nano sized model: https://huggingface.co/akridge/yolo11n-sea-urchin-detector
• link to medium sized model: https://huggingface.co/akridge/yolo11m-sea-urchin-detector

Model Weights

The model's weights can be found here | Also available in various formats:

• PyTorch (best.pt): Standard format for PyTorch-based applications.

Intended Use

• Real-time detections on underwater footage
• Post-processed video/imagery for detecting sea urchins in underwater environments

Factors

Model Performance

• Multi-source Dataset: Trained on datasets that include urchin images from various angles.
• Model Architecture (YOLO11x): Lightweight and optimized for real-time urchin detection in underwater footage.
• Training Data: The dataset is split into 70% training, 20% validation, and 10% test data.
• Training Parameters: Configured with 100 epochs, a 0.001 learning rate, and 640x640 image size for convergence.

Datasets

The training data was collected, parsed and organized from open sources:

1. Orange-OpenSource Marine-Detect
2. Roboflow - Sakana Urchins CJLib
3. Roboflow - Diad 3 Computer Vision Project

• Roboflow Details:
  • License: CC BY 4.0

Dataset Composition:

• Images: 5000
• Train/Val/Test Split Ratio: 7:2:1

Metrics

Below are the key metrics from the model evaluation on the validation set:

Training Validation Results

Training and Validation Losses

Training and Validation Losses

Confusion Matrix

Confusion Matrix

Precision-Recall Curve

Precision-Recall Curve

F1 Score Curve

F1 Score Curve

Training Configuration

• Nvidia 4070 Geoforce RTX Super
• Model Weights File: yolo11x_urchin_trained.pt
• Number of Epochs: 100
• Learning Rate: 0.001
• Batch Size: 16
• Image Size: 640x640

Deployment

How to Use the Model

To use the trained model, follow these steps:

1. Load the Model:

   from ultralytics import YOLO
   
   # Load the model
   model = YOLO("yolo11x_urchin_trained.pt")
   

Limitations

The model was trained on a mix of open source images. It may not generalize well to other environments or non-marine scenarios. Additionally, environmental variations, occlusions, or poor lighting may affect performance.

Additional Notes:

Dataset Sources:

• Two datasets were combined to improve model robustness, allowing the model to adapt to varying lighting and water conditions. Ethical Considerations:
• The detection results should be validated before using them for critical applications. The model’s performance in new environments might vary, and it may have biases if certain types of sea urchins were underrepresented in the training datasets.

Disclaimer

This repository is a scientific product and is not official communication of the National Oceanic and Atmospheric Administration, or the United States Department of Commerce. All NOAA project content is provided on an ‘as is’ basis and the user assumes responsibility for its use. Any claims against the Department of Commerce or Department of Commerce bureaus stemming from the use of this project will be governed by all applicable Federal law. Any reference to specific commercial products, processes, or services by service mark, trademark, manufacturer, or otherwise, does not constitute or imply their endorsement, recommendation or favoring by the Department of Commerce. The Department of Commerce seal and logo, or the seal and logo of a DOC bureau, shall not be used in any manner to imply endorsement of any commercial product or activity by DOC or the United States Government.

train/weights/last.pt

pt · 109 MB · SHA-256 a1828253c878…a353 · Hugging Face