new-overlay-model-yolo

YOLO Overlay Detection Model - Optimized

This model was trained to detect and segment overlay elements in images/videos using YOLOv8 segmentation with optimized hyperparameters.

Model Details

• Model Type: YOLOv8 Instance Segmentation
• Architecture: auto
• Framework: Ultralytics YOLO
• Training Date: 2025-11-07
• Task: Instance Segmentation
• Classes: Overlay elements
• Image Size: 800px (optimized for detail)

Performance Metrics

Key Optimizations

This model includes several optimizations over the baseline:

• ✅ Mosaic Augmentation enabled (1.0) - Critical for YOLO performance
• ✅ Copy-Paste Augmentation (0.3) - Essential for segmentation tasks
• ✅ Larger Image Size (800px) - Better detail capture
• ✅ Cosine LR Scheduler - Smoother convergence
• ✅ Multi-Scale Training - Better scale invariance
• ✅ Enhanced Augmentations - Rotation (10°), Scale (0.5), Perspective
• ✅ Optimized Batch Size (32) - Better gradient estimates on dual GPUs

Usage

Installation

pip install ultralytics

Inference

from ultralytics import YOLO
from huggingface_hub import hf_hub_download

# Download model
model_path = hf_hub_download(
    repo_id="farazv2/overlay-model-yolo",
    filename="best.pt"
)

# Load model
model = YOLO(model_path)

# Run inference
results = model('image.jpg')

# Process results
for result in results:
    boxes = result.boxes  # Bounding boxes
    masks = result.masks  # Segmentation masks
    
    # Visualize
    result.show()
    
    # Save
    result.save('output.jpg')

Batch Inference

# Process multiple images
results = model(['image1.jpg', 'image2.jpg', 'image3.jpg'])

# Process video
results = model('video.mp4', save=True)

Training Configuration

Model Export

The model can be exported to various formats:

from ultralytics import YOLO

model = YOLO('best.pt')

# Export to ONNX
model.export(format='onnx')

# Export to TensorRT
model.export(format='engine')

# Export to CoreML
model.export(format='coreml')

Citation

If you use this model, please cite:

@software{overlay_yolo_model,
  author = {farazv2},
  title = {YOLO Overlay Detection Model - Optimized},
  year = {2025},
  publisher = {HuggingFace},
  url = {https://huggingface.co/farazv2/overlay-model-yolo}
}

License

This model is released under the AGPL-3.0 license, following Ultralytics YOLOv8 licensing.

Acknowledgments

• Built with Ultralytics YOLOv8
• Trained on Kaggle with GPU acceleration
• Optimized with best practices for segmentation tasks