metadata

license: mit
library_name: ultralytics
tags:
  - yolov10
  - object-detection
  - computer-vision
  - pytorch
  - kitti
  - autonomous-driving
  - from-scratch
pipeline_tag: object-detection
datasets:
  - kitti
widget:
  - src: >-
      https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bounding-boxes-sample.png
    example_title: Sample Image
model-index:
  - name: yolov10-kitti-vanilla
    results:
      - task:
          type: object-detection
        dataset:
          type: kitti
          name: KITTI Object Detection
        metrics:
          - type: mean_average_precision
            name: mAP
            value: TBD

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