YOLOv8m Finetuned on RDD2022 Road Damage

Fine-tuned YOLOv8m object detector on the RDD2022 Road Damage 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.

RDD2022 Road Damage Detection Demo

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/rdd2022-yolov8m",
    filename="best.pt"
)

model = YOLO(weights)

Run Inference

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

results[0].show()

Performance

Evaluated on the RDD2022 Road Damage test split, using DetectionBench's standard evaluation pipeline (detectionbench-evaluate).

Metric	Score (%)
mAP@50	62.03
mAP@50-95	34.08
Precision	65.61
Recall	57.42
F1 Score	61.24
Parameters	25.9M
FLOPs	78.9B (at 640 px)

RDD2022 Road Damage Model Zoo

Every model DetectionBench has trained and evaluated on RDD2022 Road Damage so far, for full transparency -- see DetectionBench for the smaller, curated comparison set used on the project README.

Model	mAP@50	mAP@50-95	Precision	Recall
RF-DETR Medium	65.08	36.02	71.18	55.98
RF-DETR Small	64.71	35.73	65.69	59.41
YOLOv8m	62.03	34.08	65.61	57.42
YOLOv8s	61.45	33.53	64.55	57.18
YOLO26s	61.27	33.3	64.42	57.13
YOLO26m	61.24	33.38	63.7	57.27
RF-DETR Nano	60.85	33.22	65.49	54.3
YOLOv8n	58.8	32.05	62.03	56.08
YOLO11x	51.05	26.54	56.64	49.54

Per-Class Performance

Class	mAP@50	mAP@50-95
longitudinal_crack	55.97	30.69
transverse_crack	55.52	26.6
alligator_crack	62.5	32.91
pothole	74.15	46.12

Dataset

This model was trained on RDD2022 Road Damage. For the full dataset description, provenance, license, and citation, see the dataset card:

https://huggingface.co/datasets/dronefreak/RDD2022

Classes

longitudinal_crack
transverse_crack
alligator_crack
pothole

Training Configuration

Setting	Value
Dataset	RDD2022 Road Damage
Framework	Ultralytics YOLO
Training Toolkit	DetectionBench
Epochs (configured max)	50
Epochs (actually trained)	50
Early Stopping Patience	10
Batch Size	16
Image Size	640
Optimizer	Adam
Initial Learning Rate	0.001
Seed	0

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
rdd2022_yolov8m_showcase.jpg
README.md

Related Resources

RDD2022 Road Damage dataset card on Hugging Face
DetectionBench -- reproducible benchmarks for modern object detectors on real-world datasets
RDD2022 paper preprint (arXiv:2209.08538)
RDD2022 project repository (official data source)

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.

Known Limitations

Not comparable to the official CRDDC2022 leaderboard: the challenge test set has no public labels, so the test split here is a held-out 15% slice (70/15/15 split) of the publicly-labelled images, merged across countries -- scores are only comparable between the models listed in this card's Model Zoo.
Class imbalance: longitudinal_crack (44.0%) is the most common class, while pothole (18.1%) and alligator_crack (17.9%) are the rarest of the four -- per-class accuracy differs noticeably between them.
Four-class taxonomy only: the source data's 5th "other" bucket (block cracks, road repairs and country-specific codes, ~6.5k boxes) was dropped to match the four damage types the CRDDC2022 challenge scores, so those damage types are not detected.
Sparse, thin targets: about a third of images contain no in-taxonomy damage (clean-road frames), with 1.5 boxes per image on average, and cracks are thin structures that are easily lost when large frames (some over 4000 pixels wide) are downscaled to the model's input size.
Uneven country and imaging-setup mix: the images come from six countries and several capture setups (smartphone, dashboard camera, drone) in very different proportions, so performance can vary substantially by country and generalization to unseen regions or damage conventions is untested.
Share-alike data: the RDD2022 images are CC BY-SA 4.0 -- see the Dataset section above for attribution and the dataset card for the full terms.

Citation

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

@article{arya2022rdd2022,
  title = {RDD2022: A multi-national image dataset for automatic Road Damage Detection},
  author = {Arya, Deeksha and Maeda, Hiroya and Ghosh, Sanjay Kumar and Toshniwal, Durga and Sekimoto, Yoshihide},
  journal = {arXiv preprint arXiv:2209.08538},
  year = {2022}
}

No official YOLOv8 research paper has been published by Ultralytics; this is their own recommended software citation instead:

@software{jocher2023yolov8,
  author = {Glenn Jocher and Ayush Chaurasia and Jing Qiu},
  title = {Ultralytics YOLOv8},
  version = {8.0.0},
  year = {2023},
  url = {https://github.com/ultralytics/ultralytics},
  license = {AGPL-3.0}
}