# 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_synchro...
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_...
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: ...
yolov7.yaml配置文件如下：# parameters nc: 80 # number of classes depth_multiple: 1.0 # ...
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。
Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors - yolov7/detect.py at main · WongKinYiu/yolov7
In order to evaluate the comprehensive classification and recall ability of the classifier, the F1 score is obtained as the harmonic average of the above two indicators. The higher the F1 score, the more effective the test method is. The F1 score is defined as follows: 𝐹1=2×𝑃×𝑅...
Classifier-based methods have been widely used in the identification of rice diseases and pests. These methods train models using machine learning algorithms, such as decision trees, support vector machines (SVMs) [4], and K-nearest neighbor (KNN) [5], to classify images based on their feature...