yolov6detect 0.3.1

Packaged version of the Yolov6 repository

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}
}