---
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:

```python
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