YOLO11n Finetuned on PKLot

Fine-tuned YOLO11n object detector on the PKLot benchmark dataset, trained and evaluated as part of DetectionBench -- a framework for reproducibly benchmarking modern object detectors with identical training recipes and evaluation metrics across multiple real-world datasets.

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Usage

Install Dependencies

pip install ultralytics huggingface_hub

Load Model from Hugging Face

from huggingface_hub import hf_hub_download
from ultralytics import YOLO

weights = hf_hub_download(
    repo_id="dronefreak/pklot-yolo11n",
    filename="best.pt"
)

model = YOLO(weights)

Run Inference

results = model.predict(
    source="image.jpg",
    conf=0.25
)

results[0].show()

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Performance

Evaluated on the PKLot test split, using DetectionBench's standard evaluation pipeline (detectionbench-evaluate).

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PKLot Model Zoo

Every model DetectionBench has trained and evaluated on PKLot so far, for full transparency -- see DetectionBench for the smaller, curated comparison set used on the project README.

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Per-Class Performance

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Dataset

This model was trained on PKLot. For the full dataset description, provenance, license, and citation, see the dataset card:

https://huggingface.co/datasets/dronefreak/PKLot

Classes

• vacant
• occupied

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Training Configuration

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Repository Contents

best.pt
results.csv
args.yaml
BoxPR_curve.png
BoxF1_curve.png
BoxP_curve.png
BoxR_curve.png
confusion_matrix.png
confusion_matrix_normalized.png
val_batch0_pred.jpg
pklot_yolo11n_showcase.jpg
README.md

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Related Resources

• PKLot dataset card on Hugging Face
• DetectionBench -- reproducible benchmarks for modern object detectors on real-world datasets
• PKLot paper (Expert Systems with Applications, doi:10.1016/j.eswa.2015.02.009)
• PKLot project website (official data source)

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Training Framework

This model was trained using DetectionBench, an open-source framework for benchmarking object detectors across multiple real-world datasets with a common pipeline.

Features include:

• A dataset-adapter registry for converting real-world datasets into a canonical format
• Identical training/evaluation recipes across model families (Ultralytics YOLO/RT-DETR, RF-DETR)
• Hardware profiling (latency, FPS, VRAM, parameters, FLOPs)
• One-command reproducibility via versioned Hydra configs

If you find this model useful, please consider starring the repository.

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Known Limitations

• Not free-form car detection: PKLot's own task is per-space classification at fixed, camera-specific locations. This adapter treats every marked space as a box to detect and classify (vacant / occupied), so scores reflect that easier, location-aware task, not general-purpose car detection or the classification literature's numbers.
• Fixed-camera memorization: all frames come from 3 stationary cameras (PUCPR, UFPR04, UFPR05), so each parking space sits at a near-identical pixel position across all 12,417 frames -- a detector can substantially memorize per-camera space layouts. Splits are grouped by (lot, capture day), so validation/test measure generalization to unseen days on seen cameras, not to new lots or camera placements. Expect very high and fast-saturating mAP@50 as a result (see mAP@50-95 for a metric that still discriminates between models).
• Very dense: about 56 boxes per image on average (median 40, up to 100 -- the PUCPR lot alone has 100+ marked spaces, so roughly a third of images hit that ceiling).
• Loosened boxes for a small subset: 7,671 labelled spaces (1.1%, all in UFPR04) have no contour polygon in the release, only a rotated rectangle, so their axis-aligned box is looser than the rest (mean IoU ~0.85 against the contour-derived boxes where both exist).
• Rare weather conditions: sunny and cloudy days dominate the capture period; rainy-day frames are comparatively rare, so performance under rain is measured on fewer examples.

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Citation

If you use this model in your research, please consider citing the dataset and the model architecture:

@article{almeida2015pklot,
  title={PKLot -- A robust dataset for parking lot classification},
  author={de Almeida, Paulo R. L. and Oliveira, Luiz S. and Britto Jr, Alceu S. and Silva Jr, Eunelson J. and Koerich, Alessandro L.},
  journal={Expert Systems with Applications},
  volume={42},
  number={11},
  pages={4937--4949},
  year={2015},
  doi={10.1016/j.eswa.2015.02.009}
}

No official YOLO11 research paper has been published by Ultralytics; the most commonly cited independent architectural analysis is used instead:

@article{khanam2024yolov11,
  title={YOLOv11: An Overview of the Key Architectural Enhancements},
  author={Khanam, Rahima and Hussain, Muhammad},
  journal={arXiv preprint arXiv:2410.17725},
  year={2024}
}