YOLOv8n Finetuned on BDD100K

Fine-tuned YOLOv8n object detector on the BDD100K 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.

Performance

Metric	Score (%)
mAP@50	51.67
mAP@50-95	29.09
Precision	70.95
Recall	46.59
F1 Score	56.25
Parameters	3.2M
FLOPs	8.7B (at 640 px)

Evaluation Protocol

Metrics reported in this model card are computed on the BDD100K test split, using DetectionBench's standard evaluation pipeline (detectionbench-evaluate).

BDD100K Model Zoo

Every model DetectionBench has trained and evaluated on BDD100K 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
YOLOv9t	52.04	29.46	71.34	46.72
YOLOv10n	51.95	29.31	71.58	46.62
YOLOv8n	51.67	29.09	70.95	46.59

Per-Class Performance

Class	mAP@50	mAP@50-95
person	61.65	30.82
rider	41.33	20.69
car	79.67	49.26
truck	61.86	44.77
bus	60.43	46.63
train	0.0	0.0
motor	37.35	17.59
bike	42.38	20.71
traffic light	64.45	24.58
traffic sign	67.55	35.87

Evaluation Visualizations

Precision-Recall Curve

F1 Curve

Confusion Matrix

Normalized Confusion Matrix

Dataset

This model was trained on BDD100K. BDD100K is released under the BDD100K license (non-commercial research and education, registration required, no redistribution), so it is not mirrored on Hugging Face. Download it from the official site (https://www.bdd100k.com/) and see the Citation section below for the dataset's paper.

Classes

person
rider
car
truck
bus
train
motor
bike
traffic light
traffic sign

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/bdd100k-yolov8n",
    filename="best.pt"
)

model = YOLO(weights)

Run Inference

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

results[0].show()

Training Configuration

Setting	Value
Dataset	BDD100K
Framework	Ultralytics YOLO
Training Toolkit	DetectionBench
Epochs (configured max)	50
Epochs (actually trained)	50
Early Stopping Patience	10
Batch Size	auto (Ultralytics AutoBatch)
Image Size	960
Optimizer	SGD
Initial Learning Rate	0.01
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
bdd100k_yolov8n_showcase.jpg
assets/demo_banner.mp4
assets/demo_banner_poster.jpg
README.md

Related Resources

DetectionBench -- reproducible benchmarks for modern object detectors on real-world datasets
BDD100K project website (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 BDD100K test-server leaderboard: the official test split has no released labels, so the test split here is BDD100K's official validation set (10,000 images) and a seeded 15% slice of the official train set is held out for validation.
Severe class imbalance: car (55.4%), traffic sign (18.6%) and traffic light (14.5%) dominate the boxes, while rider (0.4%), motor (0.2%) and especially train (about 150 boxes in the whole dataset) are rare -- per-class accuracy on those classes is measured on very few examples and is close to noise for train.
Small objects: the median box covers only 0.09% of the 1280x720 frame, and traffic lights and signs are the smallest and hardest classes (medians of roughly 16 px and 21 px at native resolution), so scores on them depend heavily on input resolution.
Detection labels only: BDD100K's lane-marking and drivable-area annotations are dropped, so these models cover the 2D object detection task only.
Conditions are not broken down: the images span weather, time-of-day and scene conditions, but scores here are aggregated over all of them, and generalization outside the US road scenes BDD100K covers is untested.
Non-commercial data with no redistribution: BDD100K is released under the BDD100K license (non-commercial research and education, registration required), so the dataset is not mirrored on Hugging Face -- obtain it from the official site and check its terms before any use beyond research.

Citation

If you use this model in your research, please consider citing:

The BDD100K dataset (see below)
The original YOLOv8n architecture (see below)
The other model architectures shown in the Model Zoo/External Comparison tables above, if you reference their results
DetectionBench, the training/evaluation framework used to produce this checkpoint

@inproceedings{yu2020bdd100k,
  title={BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning},
  author={Yu, Fisher and Chen, Haofeng and Wang, Xin and Xian, Wenqi and Chen, Yingying and Liu, Fangchen and Madhavan, Vashisht and Darrell, Trevor},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={2636--2645},
  year={2020}
}

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}
}

Other architectures compared against on BDD100K in this model card:

YOLOv10

@article{wang2024yolov10,
  title={YOLOv10: Real-Time End-to-End Object Detection},
  author={Wang, Ao and Chen, Hui and Liu, Lihao and Chen, Kai and Lin, Zijia and Han, Jungong and Ding, Guiguang},
  journal={arXiv preprint arXiv:2405.14458},
  year={2024}
}

YOLOv9

@article{wang2024yolov9,
  title={YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information},
  author={Wang, Chien-Yao and Yeh, I-Hau and Liao, Hong-Yuan Mark},
  journal={arXiv preprint arXiv:2402.13616},
  year={2024}
}

@software{Saksena_DetectionBench_2026,
  author = {Saksena, Saumya Kumaar},
  title = {DetectionBench: Reproducible Benchmarks for Modern Object Detectors on Real-World Datasets},
  url = {https://github.com/dronefreak/DetectionBench},
  year = {2026}
}