README.md

---
language:
- en
base_model:
- Ultralytics/YOLO11
tags:
- yolo
- yolo11
- yolo11n
- yolo11n-seg
- fish
- Segmentation
datasets:
- akridge/MOUSS_fish_segment_dataset_grayscale
pipeline_tag: image-segmentation
---

# 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
![GIF description](./yolo11n-seg.gif)

## Model Weights
Download the model weights [here](./yolo11n-seg-fish-trained.pt)

## 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
![Confusion Matrix](./train/results.png)

## How to Use the Model
To use the trained YOLO11n-seg model for fish segmentation:

1. **Load the Model**:
```python
   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()
```