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:
- 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 weightsargs.yaml – Training configurationresults.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
