Instructions to use GaryFer/smart-parking-weights with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use GaryFer/smart-parking-weights with ultralytics:
from ultralytics import YOLOvv8 model = YOLOvv8.from_pretrained("GaryFer/smart-parking-weights") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
File size: 1,116 Bytes
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license: apache-2.0
pipeline_tag: object-detection
datasets:
- GaryFer/smart-parking-upeu-v4
language:
- es
metrics:
- map
tags:
- object-detection
- parking
- yolov8
- yolov11
- yolov12
- rtdetr
- faster-rcnn
- ultralytics
---
# Smart Parking UPeU — Model Weights
Trained model weights for parking slot detection comparing five architectures on the Smart Parking UPeU v4 dataset (Juliaca, Peru).
## Models included
- YOLOv8s
- YOLOv11s
- YOLOv12s
- RT-DETR-L
- Faster R-CNN (ResNet-50 FPN)
## Dataset
3 classes: `libre`, `ocupado`, `no_disponible`
3,072 training images / 293 validation / 146 test
## Results (mAP@0.5, mean ± SD, 10 runs)
| Model | mAP@0.5 | FPS |
|-------|---------|-----|
| YOLOv8s | 0.9948 ± 0.0002 | 205.5 ± 6.4 |
| YOLOv11s | 0.9947 ± 0.0001 | 161.2 ± 5.7 |
| YOLOv12s | 0.9946 ± ? | 94.2 ± 26.9 |
| RT-DETR-L | 0.9946 ± 0.0002 | 41.1 ± 0.6 |
| Faster R-CNN | 0.9925 ± 0.0003 | 26.9 ± 0.9 |
## Citation
```
@article{yunganina2026smartparking,
title={Smart Parking Detection...},
author={Yunganina Mamani, Gary Fernando},
year={2026}
}
``` |