100,000+ driving images with diverse weather and lighting conditions
Designed for autonomous driving applications
Contains urban driving scenarios from multiple cities
Annotations include bounding boxes for vehicles, pedestrians, and traffic elements

Usage

This model can be used with the Ultralytics YOLOv10 framework:

python

from ultralytics import YOLO

# Load the model
model = YOLO('path/to/best.pt')

# Run inference
results = model('path/to/image.jpg')

# Process results
for result in results:
    boxes = result.boxes.xyxy   # bounding boxes
    scores = result.boxes.conf  # confidence scores
    classes = result.boxes.cls  # class predictions

Model Performance

This model was trained from scratch on the Berkeley DeepDrive (BDD) 100K dataset using YOLOv10 architecture.

Intended Use

Primary Use: Object detection in autonomous driving scenarios
Suitable for: Research, development, and deployment of object detection systems
Limitations: Performance may vary on images significantly different from the training distribution

Citation

If you use this model, please cite:

bibtex

@article{yolov10,
  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}
}

License

This model is released under the MIT License.

Keywords

YOLOv10, Object Detection, Computer Vision, BDD 100K, Autonomous Driving, Deep Learning

HugoHE/yolov10-bdd-vanilla

YOLOv10 - Berkeley DeepDrive (BDD) 100K Vanilla

Model Details

Dataset Information

Dataset-specific Details: