# Apply NMS pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms) t2 = time_synchronized() 1. 2. 3. 是否对预测结果进行二次预测分类 # Apply Classifier if classify: pred = apply_classifier(pred, modelc, img, im0s) 1. 2. 3...
from utils.general import ( check_img_size, non_max_suppression, apply_classifier, scale_coords, xyxy2xywh, plot_one_box, strip_optimizer, set_logging) from utils.torch_utils import select_device, load_classifier, time_synchronized def detect(save_img=False): # 获取设置的参数数据 out, source...
from utils.general import check_img_size, check_requirements, check_imshow, non_max_suppression, apply_classifier, \ scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path from utils.plots import plot_one_box from utils.torch_utils import select_device, load_classifier, time_synch...
from utils.general import check_img_size, check_requirements, check_imshow, non_max_suppression, apply_classifier, \@@ -31,7 +37,11 @@ def detect(save_img=False): half = device.type != 'cpu' # half precision only supported on CUDA # Load model- model = attempt_load(weights, map_...
首先在coco验证集上eval一下官方开的yolov7.pt，其实是L版本(类似YOLOv5-L,YOLOX-L,PPYOLOE-L)va...
self.apply(self._init_weights) self.head.weight.data.mul_(head_init_scale) self.head.bias.data.mul_(head_init_scale) def _init_weights(self, m): if isinstance(m, (nn.Conv2d, nn.Linear)): trunc_normal_(m.weight, std=.02)
agnostic=self.agnostic_nms)t3=time_synchronized()# Apply Classifierifclassify:pred=apply_classifier(...
pred = apply_classifier(pred, modelc, img, im0s)# Process detectionsfori, detinenumerate(pred):# detections per imageifwebcam:# batch_size >= 1p, s, im0, frame = path[i],'%g: '% i, im0s[i].copy(), dataset.countelse: ...
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2.标签分配策略采用的是YOLOV5的跨网格搜索,以及YOLOX的匹配策略。 3.提出的一个新的E-ELAN高效网络架构,以高效为主。 4.提出了辅助头的一个训练方法RepConv层,主要目的是通过增加训练成本,提升精度,同时不影响推理的时间,因为辅助头只会出现在训练过程中。