HugoHE/yolov10-bdd-finetune

YOLOv10 - Berkeley DeepDrive (BDD) 100K Vanilla

YOLOv10 model fine-tuned on Berkeley DeepDrive (BDD) 100K dataset to mitigate hallucination on out-of-distribution data in autonomous driving scenarios.

Model Details

• Model Type: YOLOv10 Object Detection
• Dataset: Berkeley DeepDrive (BDD) 100K
• Training Method: fine-tuned to mitigate hallucination on out-of-distribution data
• Framework: PyTorch/Ultralytics
• Task: Object Detection

Dataset Information

This model was trained on the Berkeley DeepDrive (BDD) 100K dataset, which contains the following object classes:

car, truck, bus, motorcycle, bicycle, person, traffic light, traffic sign, train, rider

Dataset-specific Details:

Berkeley DeepDrive (BDD) 100K Dataset:

• 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:

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 fine-tuned to mitigate hallucination on out-of-distribution data on the Berkeley DeepDrive (BDD) 100K dataset using YOLOv10 architecture.

Fine-tuning Objective: This model was specifically fine-tuned to mitigate hallucination on out-of-distribution (OOD) data, improving robustness when encountering images that differ from the training distribution.

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, BDD 100K, Autonomous Driving, Deep Learning