metadata

language:
  - en
base_model:
  - Ultralytics/YOLO11
tags:
  - yolo
  - yolo11
  - yolo11n
  - yolo11n-seg
  - fish
datasets:
  - akridge/MOUSS_fish_imagery_dataset_grayscale_small

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)

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.

Model Weights

Download the model weights here

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:

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()