YOLOv10 - KITTI Object Detection Vanilla
YOLOv10 model fine-tuned on KITTI dataset to mitigate hallucination on out-of-distribution data for enhanced autonomous driving object detection.
Model Details
- Model Type: YOLOv10 Object Detection
- Dataset: KITTI Object Detection
- 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 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 fine-tuned to mitigate hallucination on out-of-distribution data on the KITTI Object Detection 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, KITTI, Autonomous Driving, Deep Learning
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