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
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:
- 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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Base model
Ultralytics/YOLO11