---
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**, **suit cases** and **handbags**  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