rectangle(img, (int(x1),int(y1)),\ (int(x2),int(y2)), (0, 255, 0), 2) # label_id = int(cls.tolist()) # roi = frame.add_region(int(top_left_x),int(top_left_y),int(abs_w),int(abs_h), self.names[label_id], conf.tolist(), normalized=False) # roi....
"do_cls_softmax": true, "iou_threshold": 0.2, "nms_threshold": 0.2, "bbox_number_on_cell": 3, "cells_number": 20, "classes": 2, "labels": ["person", "head"], "anchors": [ 10.0, 13.0, 16.0, 30.0, 33.0, 23.0, 30.0, 61.0, 62.0, 45.0, 59.0, 119.0, 116.0, 90.0, 156.0...
{ 'agnostic_nms': False, 'amp': True, 'augment': False, 'auto_augment': 'randaugment', 'batch': 64, 'bgr': 0.0, 'box': 7.5, 'cache': False, 'cfg': None, 'classes': None, 'close_mosaic': 10, 'cls': 0.5, 'conf': None, 'copy_paste': 0.15, 'copy_paste_mode': 'flip...
结论:感觉模型大了,例如,48.1M参数的YOLOv5x-cls模型,训练速度就比yolov5m-cls明显慢多了;大模型训练,不仅考虑显存大小,也要考虑显卡的CUDA核心数量。基于ImageNet 1k数据集在RTX3060上训练YOLOv5m-cls 20个Epoch,用时35.081小时,如下图所示: YOLOv5m-cls on imagement@3060...
rectangle(img, (int(x1),int(y1)),\ (int(x2),int(y2)), (0, 255, 0), 2) # label_id = int(cls.tolist()) # roi = frame.add_region(int(top_left_x),int(top_left_y),int(abs_w),int(abs_h), self.names[label_id], conf.tolist(),...
gvainference model=./models/yolov5m_openvino_model/yolov5m.xml device=CPU inference-interval=1 model_proc=./models/model_proc/yolo-v5_80-raw.json name=gvainference inference-region=full-frame \# ! queue ! gvatrack tracking-type=short-term-imageless \# ! queue ! gvawa...
@@ -413,22 +413,22 @@ python export.py --weights yolov5s-cls.pt resnet50.pt efficientnet_b0.pt --inclu