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---
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
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