NMFS-OSI/yolo11m-esa-coral-icra-detector

The Yolo11m ESA Coral ICRA Detector is a lightweight object detection model designed to identify Isopora crateriformis in underwater optical imagery. It uses the YOLO11 architecture and was trained on NOAA datasets with various augmentations to improve performance in diverse underwater conditions.

This model was trained to identify and locate Isopora crateriformis (ICRA) using the ultralytics YOLO11 architecture in various underwater conditions. - Model Architecture: YOLO11m - Task: Object Detection (Detection) - Classes: 1 - ICRA (Isopora crateriformis) - Data Type: Underwater Optical Imagery

• Detection of ICRA on underwater optical data

The training data was collected, parsed, sorted, augmented, and organized from various NOAA Missions: - Google Cloud Link: Dataset - METADATA

Dataset Composition:

• Multi-source Dataset: Trained on datasets that include images from various angles.
• TOAL Images: 470 images
• Training Images: 327
• Validation Images: 71
• Test Images: 72
• Train/Val/Test Split Ratio: 7:2:1

Data Augmentations Used

To improve generalization in underwater environments and to increase dataset size, the following augmentations were applied: - Mosaic: Enabled (Closed last 40 epochs). - HSV Color Jitter: Hue (0.015), Saturation (0.4), Value (0.3). - Scaling: Random scaling (0.5). - MixUp: Disabled to prevent unrealistic object overlap.

• Model Weights File: yolo11-esa-icra-detector.pt
• Number of Epochs: 100
• Learning Rate: Optimizer AdamW (Learning Rate: 0.001, Final LR: 0.00003)
• Batch Size: 16
• Image Size: 1024x1024

How to Use the Model

To use the trained model, follow these steps: 1. Load the Model: ```python from ultralytics import YOLO

# Load the model model = YOLO("yolo11-esa-icra-detector.pt") results = model.predict("path/to/image_or_video.jpg", imgsz=1024, conf=0.5) ```

It may not generalize well to other environments not found in training dataset(Am.Samoa) or non-marine scenarios. Additionally, environmental variations, occlusions, or poor lighting may affect performance.

Additional Notes:

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 corals were underrepresented in the training datasets.