HugoHE/yolov10-kitti-vanilla

YOLOv10 - KITTI Object Detection Vanilla

YOLOv10 model trained from scratch on KITTI dataset for autonomous driving object detection.

Model Details

• Model Type: YOLOv10 Object Detection
• Dataset: KITTI Object Detection
• Training Method: trained from scratch
• Framework: PyTorch/Ultralytics
• Task: Object Detection

Dataset Information

This model was trained on the KITTI Object Detection dataset, which contains the following object classes:

car, pedestrian, cyclist

Dataset-specific Details:

KITTI Object Detection Dataset:

• Real-world autonomous driving dataset
• Contains stereo imagery from vehicle-mounted cameras
• Focus on cars, pedestrians, and cyclists
• Challenging scenarios with varying lighting and weather conditions

Usage

This model can be used with the Ultralytics YOLOv10 framework:

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:

@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