metadata

license: mit
library_name: ultralytics
tags:
  - yolov10
  - object-detection
  - computer-vision
  - pytorch
  - bdd100k
  - autonomous-driving
  - BDD 100K
  - from-scratch
pipeline_tag: object-detection
datasets:
  - bdd100k
base_model: YOLOv10
widget:
  - src: >-
      https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bounding-boxes-sample.png
    example_title: Sample Image
model-index:
  - name: yolov10-bdd-finetune
    results:
      - task:
          type: object-detection
        dataset:
          type: bdd100k
          name: Berkeley DeepDrive (BDD) 100K
        metrics:
          - type: mean_average_precision
            name: mAP
            value: TBD

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