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 KITTI Object Detection 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, KITTI, Autonomous Driving, Deep Learning

HugoHE/yolov10-kitti-vanilla

YOLOv10 - KITTI Object Detection Vanilla

Model Details

Dataset Information

Dataset-specific Details:

Usage

Model Performance

Intended Use

Citation

License

Keywords

HugoHE/
yolov10-kitti-vanilla