11、load_classifier 12、scale_img 13、de_parallel 14、is_parallel、copy_attr、ModelEMA类 14.1、is_parallel 14.2、copy_attr 14.3、class ModelEMA 总结 前言 源码: YOLOv5源码. 注释版全部项目文件已上传至GitHub: yolov5-5.x-annotations. 这个文件主要是基于torch的一些实用工具类,整个项目的文件都可能...
model.half() # to FP16 # Second-stage classifier classify = False if classify: modelc = load_classifier(name='resnet101', n=2) # initialize modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']).to(device).eval() # Set Dataloader vid_path, vid_...
model.half() # to FP16 if classify: # second-stage classifier modelc = load_classifier(name='resnet50', n=2) # initialize modelc.load_state_dict(torch.load('resnet50.pt', map_location=device)['model']).to(device).eval() elif onnx: check_requirements(('onnx', 'onnxruntime'))...
map_location=device)# load FP32 modelstride=int(model.stride.max())# model strideimgsz=check_img_size(imgsz,s=stride)# check img_sizeifhalf:model.half()# to FP16# Second-stage classifierclassify=Falseifclassify:modelc=load_classifier(name='resnet101',n=2)# initializemodelc.load_state_...
比如做车牌的识别,先识别出车牌 ,如果想对车牌上的字进行识别,就需要二级分类进一步检测。如果对模型输出的分类再进行分类,就可以用这个模块。不过这里这个类写的比较简单,若进行复杂的二级分类,可以根据自己的实际任务可以改写,这里代码不唯一。这里的功能和torch_utils.py中的load_classifier函数功能相似。
if classify: # second-stage classifier modelc = load_classifier(name='resnet50', n=2) # initialize modelc.load_state_dict(torch.load('resnet50.pt', map_location=device)['model']).to(device).eval() elif onnx: if dnn: # check_requirements(('opencv-python>=4.5.4',)) ...
xyxy2xywh, strip_optimizer, set_logging, increment_pathfromutils.plotsimportplot_one_boxfromutils.torch_utilsimportselect_device, load_classifier, time_synchronized device= select_device('') augment=False conf_thres=0.15iou_thres=0.25model= attempt_load('yolov5s.pt', map_location=device) ...
xyxy2xywh, plot_one_box, strip_optimizer, set_logging)fromutils.torch_utilsimportselect_device, load_classifier, time_synchronizeddefdetect(save_img=False):#获取设置的参数数据out, source, weights, view_img, save_txt, imgsz =\ opt.save_dir, opt.source, opt.weights, opt.view_img, opt.save...
from utils.torch_utils import select_device, load_classifier, time_synchronized def detect(save_img...
classify=Falseifclassify:modelc=load_classifier(name='resnet101',n=2)# initialize modelc.load_state_dict(torch.load('weights/resnet101.pt',map_location=device)['model']).to(device).eval()# Set Dataloader vid_path,vid_writer=None,Noneifwebcam:view_img=check_imshow()cudnn.benchmark=True...