YOLO11n Finetuned on BDD100K Weather Classification

Fine-tuned YOLO11n image classifier on the BDD100K Weather Classification dataset, trained and evaluated as part of BDD100K-Toolkit, a dependency-clean toolkit for preparing BDD100K, training models on it and evaluating them with the same metrics on the same splits.

7-class weather classification (clear / partly cloudy / overcast / rainy / snowy / foggy / unknown) derived from BDD100K's per-image attributes.weather field. Unofficial task; follows the Kaggle dataset of the same name.

Usage

from huggingface_hub import hf_hub_download
from ultralytics import YOLO

model = YOLO(hf_hub_download("dronefreak/bdd100k-weather-yolo11n-cls", "best.pt"))
probs = model("street.jpg", verbose=False)[0].probs
print(model.names[probs.top1], f"{float(probs.top1conf):.1%}")

Results

Evaluated on the test split (10000 images).

Per class

This checkpoint classifies none of the test images of foggy (13 images) correctly, so treat it as unsupported.

Model Zoo

All runs below were evaluated on the same test split, sorted by top-1 accuracy.

Training

Dataset

dronefreak/BDD100K-Weather-Classification holds the prepared splits these models were trained and evaluated on.

Limitations

• Unofficial task: labels are BDD100K's per-image attributes, not a benchmark with a public leaderboard, so scores are only comparable with other models evaluated on this split.
• Not the official test set: test here is BDD100K's official validation split (the official test labels are not released) and valid is a seeded 15% slice of the official train split.
• Heavily imbalanced: rare classes have very few test images, so their per-class scores are noisy. Prefer macro F1 over accuracy.
• Images are US dashcam frames; generalization to other regions or camera setups is untested.
• BDD100K is released for non-commercial research and education. Check its license before any use of these weights beyond research.
• clear, partly cloudy and overcast are subjective annotator calls, so expect label noise between neighbouring classes.

License

The weights are released under the license in the metadata above. They were trained on BDD100K, which is free for non-commercial research and education; commercial use needs separate permission (see https://www.bdd100k.com/). The prepared dataset on the Hub is tagged license: other, and the original BDD100K terms still apply.

Citation

Model

Ultralytics has not published a YOLO11 paper; this independent overview covers its architecture.

@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}
}

Dataset

@article{yu2018bdd100k,
  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},
  journal={arXiv preprint arXiv:1805.04687},
  year={2018}
}

Files

• best.pt
• metrics.json
• results.csv
• results.png
• confusion_matrix.png
• confusion_matrix_normalized.png
• val_batch0_pred.jpg
• assets/demo_banner.mp4
• assets/demo_banner_poster.jpg
• README.md

Reproduce

Trained and evaluated with BDD100K-Toolkit: bdd100k-evaluate --dataset bdd100k-weather --checkpoint <weights> --data-dir <prepared dir> --split test.