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README.md

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+---
+tags:
+- yolo
+- yolov8
+- segmentation
+- overlay-detection
+- computer-vision
+- instance-segmentation
+library_name: ultralytics
+license: agpl-3.0
+---
+
+# 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
+
+| Metric | Value |
+|--------|-------|
+| Box mAP@0.5 | 0.9038 |
+| Box mAP@0.5:0.95 | 0.7171 |
+| Mask mAP@0.5 | 0.3981 |
+| Mask mAP@0.5:0.95 | 0.1520 |
+
+
+## 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
+
+```bash
+pip install ultralytics
+```
+
+### Inference
+
+```python
+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
+
+```python
+# Process multiple images
+results = model(['image1.jpg', 'image2.jpg', 'image3.jpg'])
+
+# Process video
+results = model('video.mp4', save=True)
+```
+
+## Training Configuration
+
+| Parameter | Value | Notes |
+|-----------|-------|-------|
+| Epochs | 10 | |
+| Image Size | 800 | Increased from 640 |
+| Batch Size | 16 | Optimized for dual T4 |
+| Optimizer | AdamW | |
+| Initial LR | 0.0005 | With cosine scheduler |
+| Mosaic | 1.0 | Re-enabled (critical!) |
+| Copy-Paste | 0.3 | New addition |
+| Multi-Scale | True | Enabled |
+| Mixed Precision | True | Enabled |
+| Patience | 25 | |
+
+
+## Model Export
+
+The model can be exported to various formats:
+
+```python
+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:
+
+```bibtex
+@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](https://github.com/ultralytics/ultralytics)
+- Trained on Kaggle with GPU acceleration
+- Optimized with best practices for segmentation tasks