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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:

Test Results

GIF description

Model Weights

Download the model weights here

Auto-Training Process

The segmentation dataset was generated using an automated pipeline:

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

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()
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