代码收藏家技术教程 2022-07-23

手把手教你运行YOLOv6（超详细）

YOLOv6 是美团视觉智能部研发的一款目标检测框架，致力于工业应用。本框架同时专注于检测的精度和推理效率，在工业界常用的尺寸模型中：YOLOv6-nano 在 COCO 上精度可达 35.0% AP，在 T4 上推理速度可达 1242 FPS；YOLOv6-s 在 COCO 上精度可达 43.1% AP，在 T4 上推理速度可达 520 FPS。在部署方面，YOLOv6 支持 GPU（TensorRT）、CPU（OPENVINO）、ARM（MNN、TNN、NCNN）等不同平台的部署，极大地简化工程部署时的适配工作。

YOLOv6 GitHub网址：美团/YOLOv6：YOLOv6：专用于工业应用的单级物体检测框架。 (github.com)

记得把对应版本的模型也下载，我下的是YOLOv6-s

终端输入 pip install requirements.txt，YOLOv6比V5多了一个addict库，也可以只下载一个addict

打开程序，找到文件夹tools->infer.py

默认的路径需要改一下，否则会显示找不到文件，不想动手的就直接复制下面的吧

#!/usr/bin/env python3
# -*- coding:utf-8 -*-
import argparse
import os
import sys
import os.path as osp

import torch

ROOT = os.getcwd()
if str(ROOT) not in sys.path:
    sys.path.append(str(ROOT))

from yolov6.utils.events import LOGGER
from yolov6.core.inferer import Inferer


def get_args_parser(add_help=True):
    parser = argparse.ArgumentParser(description='YOLOv6 PyTorch Inference.', add_help=add_help)
    parser.add_argument('--weights', type=str, default='../weights/yolov6s.pt', help='model path(s) for inference.')
    parser.add_argument('--source', type=str, default='../data/images', help='the source path, e.g. image-file/dir.')
    parser.add_argument('--yaml', type=str, default='../data/coco.yaml', help='data yaml file.')
    parser.add_argument('--img-size', type=int, default=640, help='the image-size(h,w) in inference size.')
    parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold for inference.')
    parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold for inference.')
    parser.add_argument('--max-det', type=int, default=1000, help='maximal inferences per image.')
    parser.add_argument('--device', default='0', help='device to run our model i.e. 0 or 0,1,2,3 or cpu.')
    parser.add_argument('--save-txt', action='store_true', help='save results to *.txt.')
    parser.add_argument('--save-img', action='store_false', help='save visuallized inference results.')
    parser.add_argument('--classes', nargs='+', type=int, help='filter by classes, e.g. --classes 0, or --classes 0 2 3.')
    parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS.')
    parser.add_argument('--project', default='runs/inference', help='save inference results to project/name.')
    parser.add_argument('--name', default='exp', help='save inference results to project/name.')
    parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels.')
    parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences.')
    parser.add_argument('--half', action='store_true', help='whether to use FP16 half-precision inference.')

    args = parser.parse_args()
    LOGGER.info(args)
    return args

@torch.no_grad()
def run(weights=osp.join(ROOT, 'yolov6s.pt'),
        source=osp.join(ROOT, 'data/images'),
        yaml=None,
        img_size=640,
        conf_thres=0.25,
        iou_thres=0.45,
        max_det=1000,
        device='',
        save_txt=False,
        save_img=True,
        classes=None,
        agnostic_nms=False,
        project=osp.join(ROOT, 'runs/inference'),
        name='exp',
        hide_labels=False,
        hide_conf=False,
        half=False,
        ):
    """ Inference process

    This function is the main process of inference, supporting image files or dirs containing images.

    Args:
        weights: The path of model.pt, e.g. yolov6s.pt
        source: Source path, supporting image files or dirs containing images.
        yaml: Data yaml file, .
        img_size: Inference image-size, e.g. 640
        conf_thres: Confidence threshold in inference, e.g. 0.25
        iou_thres: NMS IOU threshold in inference, e.g. 0.45
        max_det: Maximal detections per image, e.g. 1000
        device: Cuda device, e.e. 0, or 0,1,2,3 or cpu
        save_txt: Save results to *.txt
        save_img: Save visualized inference results
        classes: Filter by class: --class 0, or --class 0 2 3
        agnostic_nms: Class-agnostic NMS
        project: Save results to project/name
        name: Save results to project/name, e.g. 'exp'
        line_thickness: Bounding box thickness (pixels), e.g. 3
        hide_labels: Hide labels, e.g. False
        hide_conf: Hide confidences
        half: Use FP16 half-precision inference, e.g. False
    """
    # create save dir
    save_dir = osp.join(project, name)
    if (save_img or save_txt) and not osp.exists(save_dir):
        os.makedirs(save_dir)
    else:
        LOGGER.warning('Save directory already existed')
    if save_txt:
        os.mkdir(osp.join(save_dir, 'labels'))

    # Inference
    inferer = Inferer(source, weights, device, yaml, img_size, half)
    inferer.infer(conf_thres, iou_thres, classes, agnostic_nms, max_det, save_dir, save_txt, save_img, hide_labels, hide_conf)

    if save_txt or save_img:
        LOGGER.info(f"Results saved to {save_dir}")


def main(args):
    run(**vars(args))


if __name__ == "__main__":
    args = get_args_parser()
    main(args)

创建一个weights文件夹，将在官网下载好的yolov6s.pt模型放进去