Yolo11n-seg Fish Segmentation

Model Overview

This model was trained to detect and segment fish in underwater Grayscale Imagery using the YOLO11n-seg architecture, leveraging automatic training with the Segment Anything Model (SAM) for generating segmentation masks. The combination of detection and SAM-powered segmentation enhances the model's ability to outline fish boundaries.

• Model Architecture: YOLO11n-seg
• Task: Fish Segmentation
• Footage Type: Grayscale Underwater Footage
• Classes: 1 (Fish)

Demo Space:

• https://huggingface.co/spaces/noaa-pifsc-ai/YOLO11_Fish-or-No-Fish-Detector-Demo_grayscale

Test Results

Model Weights

Download the model weights here

Auto-Training Process

The segmentation dataset was generated using an automated pipeline:

• Detection Model: A pre-trained YOLO model (https://huggingface.co/akridge/yolo11-fish-detector-grayscale/) was used to detect fish.
• Segmentation: The SAM model (sam_b.pt) was applied to generate precise segmentation masks around detected fish.
• Output: The dataset was saved at /content/sam_dataset/.

This automated process allowed for efficient mask generation without manual annotation, facilitating faster dataset creation.

Intended Use

• Real-time fish detection and segmentation on grayscale underwater imagery.
• Post-processing of video or images for research purposes in marine biology and ecosystem monitoring.

Training Configuration

• Dataset: SAM asisted segmentation dataset.
• Training/Validation Split: 80% training, 20% validation.
• Number of Epochs: 50
• Learning Rate: 0.001
• Batch Size: 16
• Image Size: 640x640

Results and Metrics

The model was trained and evaluated on the generated segmentation dataset with the following results:

Confusion Matrix

How to Use the Model

To use the trained YOLO11n-seg model for fish segmentation:

1. Load the Model:

   from ultralytics import YOLO

   # Load YOLO11n-seg model
   model = YOLO("yolo11n_fish_seg_trained.pt")

   # Perform inference on an image
   results = model("/content/test_image.jpg")
   results.show()