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--- |
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language: |
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- en |
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base_model: |
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- Ultralytics/YOLO11 |
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tags: |
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- yolo |
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- yolo11 |
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- yolo11n |
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- yolo11n-seg |
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- fish |
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datasets: |
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- akridge/MOUSS_fish_imagery_dataset_grayscale_small |
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--- |
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# Yolo11n-seg Fish Segmentation |
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## Model Overview |
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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. |
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- **Model Architecture**: YOLO11n-seg |
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- **Task**: Fish Segmentation |
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- **Footage Type**: Grayscale Underwater Footage |
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- **Classes**: 1 (Fish) |
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## Test Results |
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![GIF description](./yolo11n-seg.gif) |
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## Model Weights |
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Download the model weights [here](./yolo11n_fish_seg_trained.pt) |
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## Auto-Training Process |
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The segmentation dataset was generated using an automated pipeline: |
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- **Detection Model**: A pre-trained YOLO model (https://huggingface.co/akridge/yolo11-fish-detector-grayscale/) was used to detect fish. |
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- **Segmentation**: The SAM model (`sam_b.pt`) was applied to generate precise segmentation masks around detected fish. |
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- **Output**: The dataset was saved at `/content/sam_dataset/`. |
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This automated process allowed for efficient mask generation without manual annotation, facilitating faster dataset creation. |
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## Intended Use |
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- Real-time fish detection and segmentation on grayscale underwater imagery. |
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- Post-processing of video or images for research purposes in marine biology and ecosystem monitoring. |
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## Training Configuration |
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- **Dataset**: SAM asisted segmentation dataset. |
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- **Training/Validation Split**: 80% training, 20% validation. |
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- **Number of Epochs**: 50 |
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- **Learning Rate**: 0.001 |
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- **Batch Size**: 16 |
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- **Image Size**: 640x640 |
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## Results and Metrics |
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The model was trained and evaluated on the generated segmentation dataset with the following results: |
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### Confusion Matrix |
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![Confusion Matrix](./train/results.png) |
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## How to Use the Model |
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To use the trained YOLO11n-seg model for fish segmentation: |
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1. **Load the Model**: |
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```python |
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from ultralytics import YOLO |
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# Load YOLO11n-seg model |
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model = YOLO("yolo11n_fish_seg_trained.pt") |
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# Perform inference on an image |
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results = model("/content/test_image.jpg") |
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results.show() |
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``` |