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README.md

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+
+---
+license: mit
+tags:
+- yolov8
+- object-detection
+- computer-vision
+- mobile-phone
+- suitcase
+---
+
+# YOLOv8n Multi-Object Detector (Phone & Suitcase)
+
+A fine-tuned **YOLOv8 Nano (YOLOv8n)** model capable of detecting **mobile phones** and **suitcases** in images.
+The model has been trained and validated on custom datasets with diverse backgrounds, lighting conditions,
+and object orientations.
+
+## Model Details
+
+- **Base Model**: YOLOv8n (Nano)
+- **Task**: Object Detection
+- **Supported Classes**:
+  - `mobile_phone`
+  - `suitcase`
+- **Number of Classes**: 2
+- **Input Resolution**: 640 × 640
+- **Framework**: Ultralytics YOLO
+- **Training Date**: 2026-02-06
+
+## Capabilities
+
+This model can:
+- Detect **mobile phones** in real-world scenes
+- Detect **suitcases / luggage** in travel and indoor environments
+- Handle multiple objects per image
+- Perform robustly under varied lighting and viewpoints
+
+## Training Details
+
+- **Image Size**: 640
+- **Optimizer**: Default YOLOv8 optimizer
+- **Augmentations**: YOLOv8 default augmentations
+- **Device**: NVIDIA GPU
+- **Training Strategy**: Transfer learning from YOLOv8n
+
+## Dataset Information
+
+The model was trained using curated datasets containing:
+- Mobile phone images with bounding-box annotations
+- Suitcase / luggage images with bounding-box annotations
+- Real-world indoor and outdoor scenes
+- Multiple object instances per image
+
+Dataset annotations follow object-detection standards compatible with YOLO training pipelines.
+
+## Evaluation & Metrics
+
+- Quantitative evaluation metrics are available in `results.csv`
+- Qualitative predictions can be seen in validation images from the training run
+
+## Files in This Repository
+
+- `Yolov8_SE_2.pt` – Trained YOLOv8 model weights
+- `args.yaml` – Training configuration
+- `results.csv` – Training and validation metrics
+
+## License
+
+This model is released under the **MIT License**.
+
+## Acknowledgements
+
+- Ultralytics YOLOv8: https://github.com/ultralytics/ultralytics
+- Training datasets sourced and curated for object detection research