conf_thres: 置信度阈值,即过滤掉小于此值的检测结果。iou_thres: NMS(非极大值抑制)的 IOU 阈值,即用于去除重叠的检测结果。 max_det: 每张图像的最大检测数,即指定每张图像最多检测多少个目标。 device: 设备类型,即指定使用的设备类型,如 CPU 或 GPU。 view_img: 显示结果,即指定是否在推理过程中显示结果。
conf_thres: a confidence threshold to filter detections nms_thres: a iou threshold to filter detections return: boxes: output after nms with the shape (x1, y1, x2, y2, conf, cls_id) """ # Get the boxes that score > CONF_THRESH boxes = prediction[prediction[:, 4] >= conf_thres]...
— img-size:推理图片的大小 — conf-thres: 对象置信阈值,默认0.4 — iou-thres: NMS的IOU阈值,可以根据实际对象的重叠度调节,默认0.5 — device: 选择使用CUDA或者CPU — view-img: 显示所有推理结果 — save-txt:将每一帧的推理结果及边界框的位置,存入*.txt文件 — classes:类别过滤,意思是只推理目标类...
conf_thres: a confidence threshold to filter detections nms_thres: a iou threshold to filter detections return: boxes: output after nms with the shape (x1, y1, x2, y2, conf, cls_id) """ # Get the boxes that score > CONF_THRESH boxes = prediction[prediction[:, 4] >= conf_thres]...
base_conf,base_iou=0.5,0.3 def det_img(cv_src,conf_thres, iou_thres): yolo_det = YOLODet.YOLODet(model, conf_thres=conf_thres, iou_thres= iou_thres) yolo_det(cv_src) cv_dst = yolo_det.draw_detections(cv_src) return cv_dst ...
/ (1. + np.exp(-x)) def postprocess( feats: List[ndarray], conf_thres: float = 0.25, reg_max: int = 16) -> Tuple: dfl = np.arange(0, reg_max, dtype=np.float32) scores_pro = [] boxes_pro = [] labels_pro = [] for i in range(3): stride = 8 << i score_feat =...
pythonexport-det.py \--weights yolov8s.pt \--iou-thres 0.65 \--conf-thres 0.25 \--topk 100 \--opset 11 \--sim \--input-shape 1 3 640 640 \--device cuda:0 9.2 转tensorRT 9.2.1 trtexec 最简单的方式是使用TensorRT的bin文件夹下的trtexec.exe可执行文件 ...
(xyxy, conf, cls) if multi_label: i, j = (x[:, 5:mi] > conf_thres).nonzero(as_tuple=False).T x = torch.cat((box[i], x[i, 5 + j, None], j[:, None].float(), mask[i]), 1) else: # best class only conf, j = x[:, 5:mi].max(1, keepdim=True) x = torch...
python build.py --weights yolov8n.onnx --iou-thres 0.65 --conf-thres 0.25 --topk 100 --fp16 --device cuda:0 等待一会,engine成功导出。 使用python脚本进行推理: python infer-det.py --engine yolov8n.engine --imgs data --show --out-dir outputs --out-dir outputs --device cuda:0 ...
import onnxruntimeclassYOLOv8:def__init__(self,path,conf_thres=0.7,iou_thres=0.7):self.conf_threshold=conf_thresself.iou_threshold=iou_thres# Initialize modelself.initialize_model(path)def__call__(self,image):returnself.detect_objects(image)definitialize_model(self,path):self.session=onnxrun...