File size: 1,386 Bytes
83b5cf2 d9de501 02eb4bb d9de501 02eb4bb d9de501 83b5cf2 d9de501 a28783d d9de501 a28783d 02eb4bb d9de501 02eb4bb d9de501 02eb4bb d9de501 02eb4bb d9de501 02eb4bb d9de501 02eb4bb d9de501 02eb4bb d9de501 02eb4bb d9de501 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 |
---
license: gpl-3.0
tags:
- object-detection
- computer-vision
- yolov6
- yolo
datasets:
- detection-datasets/coco
language:
- en
pipeline_tag: object-detection
library_name: yolov6detect
library_version: 0.2.3
---
### Model Description
[YOLOv6:](https://arxiv.org/abs/2209.02976) A single-stage object detection framework dedicated to industrial applications.
[YOLOv6 v3.0](https://arxiv.org/abs/2301.05586): A Full-Scale Reloading
[YOLOv6-Pip: Packaged version of the Yolov6 repository](https://github.com/kadirnar/yolov6-pip/)
[Paper Repo: Implementation of paper - YOLOv6](https://github.com/meituan/YOLOv6/)
### Installation
```
pip install yolov6detect
```
### Yolov6 Inference
```python
from yolov6 import YOLOV6
model = YOLOV6(weights='kadirnar/yolov6l6-v3.0', device='cuda:0', hf_model=True)
model.classes = None
model.conf = 0.25
model.iou = 0.45
model.show = False
model.save = True
pred = model.predict(source='data/images',yaml='data/coco.yaml', img_size=640)
```
### BibTeX Entry and Citation Info
```
@article{li2022yolov6,
title={YOLOv6: A single-stage object detection framework for industrial applications},
author={Li, Chuyi and Li, Lulu and Jiang, Hongliang and Weng, Kaiheng and Geng, Yifei and Li, Liang and Ke, Zaidan and Li, Qingyuan and Cheng, Meng and Nie, Weiqiang and others},
journal={arXiv preprint arXiv:2209.02976},
year={2022}
}
``` |