Yolov6-Pip: Packaged version of the Yolov6 repository
Overview
This repo is a packaged version of the Yolov6 model.
Benchmark
| Model | Size | mAPval 0.5:0.95 |
SpeedT4 trt fp16 b1 (fps) |
SpeedT4 trt fp16 b32 (fps) |
Params (M) |
FLOPs (G) |
|---|---|---|---|---|---|---|
| YOLOv6-N | 640 | 37.5 | 779 | 1187 | 4.7 | 11.4 |
| YOLOv6-S | 640 | 45.0 | 339 | 484 | 18.5 | 45.3 |
| YOLOv6-M | 640 | 50.0 | 175 | 226 | 34.9 | 85.8 |
| YOLOv6-L | 640 | 52.8 | 98 | 116 | 59.6 | 150.7 |
| YOLOv6-N6 | 1280 | 44.9 | 228 | 281 | 10.4 | 49.8 |
| YOLOv6-S6 | 1280 | 50.3 | 98 | 108 | 41.4 | 198.0 |
| YOLOv6-M6 | 1280 | 55.2 | 47 | 55 | 79.6 | 379.5 |
| YOLOv6-L6 | 1280 | 57.2 | 26 | 29 | 140.4 | 673.4 |
Installation
pip install yolov6detect
Yolov6 Inference
from yolov6 import YOLOV6
model = YOLOV6(weights='yolov6s.pt', device='cuda:0')
#model = YOLOV6(weights='kadirnar/yolov6t-v2.0', 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)Citation
@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}
}
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