这里卷积层,BatchNorm层,和一个LeakyReLU组成一个modules然后append到model_list里面,output_filters用于记录filters以便后面使用。 我在看这个代码的时候看这个route层很不理解,看了两篇博客,这里加上自己的理解,其实我在上面也加上了, 这里面的route主要用于连接,yolov3为了效果更好,参考了其他object-detection的做法,...
model=Darknet(cfg,arc=opt.arc).to(device) 然后沿着Darknet实现进行讲解: 代码语言:javascript 复制 classDarknet(nn.Module):# YOLOv3 object detection model def__init__(self,cfg,img_size=(416,416),arc='default'):super(Darknet,self).__init__()self.module_defs=parse_model_cfg(cfg)self.mo...
class Darknet(nn.Module): """YOLOv3 object detection model""" def __init__(self, config_path, img_size=416): super(Darknet, self).__init__() self.module_defs = parse_model_config(config_path) #模型中的参数定义,通过这个函数将配置文件中的块存储为列表形式,属性与值一一对应 self.hyper...
classDarknet(nn.Module):# YOLOv3 object detection modeldef__init__(self, cfg, img_size=(416,416), verbose=False):super(Darknet, self).__init__() self.module_defs = parse_model_cfg(cfg) self.module_list, self.routs = create_modules(self.module_defs, img_size, cfg) self.yolo_lay...
from utils.parse_config import parse_model_config 1. 2. 3. 4. 当然,除了上面这些,还需要一些模块,后面会讲 建立Darknet类,此为YOLOv3模型的主干,先在初始化函数中,写出模型所需要的属性 class Darknet(nn.Module): """YOLOv3 object detection model""" ...
# Initialize modelmodel = Darknet(cfg, arc=opt.arc).to(device) 然后沿着Darknet实现进行讲解: classDarknet(nn.Module):# YOLOv3 object detection modeldef__init__(self, cfg, img_size=(416,416), arc='default'):super(Darknet, self).__init__() ...
model = Darknet(cfg, arc=opt.arc).to(device) 然后沿着Darknet实现进行讲解: classDarknet(nn.Module): # YOLOv3 object detection model def__init__(self, cfg, img_size=(416,416), arc='default'): super(Darknet, self).__init__
#读取图片开始预测 photo_file = 'zebra.jpg' make_predict(photo_file) 结果如下: 参考文献: YOLOv3 论文 How to Perform Object Detection With YOLOv3 in Keras How to implement a YOLO (v3) object detector from scratch in PyTorch YOLOv3网络结构和解析...
_, small_object_output = self.backblock2(con3)returnbig_object_output, medium_object_output, small_object_output class DetectionBlock 负责对YOLOv3的输出下图中的计算: classDetectionBlock(nn.Cell):""" YOLOv3 detection Network. It will finally output the detection result. ...
when I run object_detection_demo_yolov3_async.py with custom trained yolov3 tiny model, I'm experiencing the following error: --- Error: /opt/intel/openvino/deployment_tools/open_model_zoo/demos/python_demos/object_detection_demo_yolov3_async...