Neural network windows SSD咖啡馆

Neural network windows SSD咖啡馆,neural-network,computer-vision,deep-learning,caffe,pycaffe,Neural Network,Computer Vision,Deep Learning,Caffe,Pycaffe,各位,当我在windows中测试时,我得到了以下错误: E:\caffe-ssd-microsoft\Build\x64\Release\pycaffe>python E:\caffe-ssd-microsoft\examples\ssd\ssd_pascal_webcam.py --cpu Traceback (most recent call last): File "E:\caffe-ssd-microsoft\examples\ssd\ssd_pascal_webc

各位,当我在windows中测试时,我得到了以下错误:

    E:\caffe-ssd-microsoft\Build\x64\Release\pycaffe>python E:\caffe-ssd-microsoft\examples\ssd\ssd_pascal_webcam.py --cpu
  Traceback (most recent call last):
  File "E:\caffe-ssd-microsoft\examples\ssd\ssd_pascal_webcam.py", line 151, in <module>
  for file in os.listdir(snapshot_dir):
     WindowsError: [Error 3] : 'models/VGGNet/VOC0712/SSD_300x300/*.*'

python中运算符部分字符串中的
'{}'


基本上,字符串中的每个
{}
都被
.format()

中相应的值所替换,您好,Shai!为什么它没有给出完整的地址?我如何解决这个问题?我是否使用了假地址?@H.Hao您是否下载了经过预培训的模型?文件夹
'models/VGGNet/VOC0712/SSD_300x300'
是否存在(请注意,这是您当前工作目录的相对路径)?我下载了模型,我的地址是models/VGGNet/VOC0712/SSD_300×300。但是在SSD_pascal_video.py中,我找不到SSD_300×300应该位于哪个文件中,我只是阅读了官方介绍,在我的地址中找到了文件。
   # The job name should be same as the name used in examples/ssd/ssd_pascal.py.
   job_name = "SSD_{}".format(resize)
   # The name of the model. Modify it if you want.
   model_name = "VGG_VOC0712_{}".format(job_name)

   # Directory which stores the model .prototxt file.
  save_dir = "models/VGGNet/VOC0712/{}_video".format(job_name)
  # Directory which stores the snapshot of trained models.
  snapshot_dir = "models/VGGNet/VOC0712/{}".format(job_name)
  # Directory which stores the job script and log file.
  job_dir = "jobs/VGGNet/VOC0712/{}_video".format(job_name)

  # model definition files.
  test_net_file = "{}/test.prototxt".format(save_dir)
  # snapshot prefix.
  snapshot_prefix = "{}/{}".format(snapshot_dir, model_name)
  # job script path.
  job_file = "{}/{}.sh".format(job_dir, model_name)

  # Find most recent snapshot.
  max_iter = 0
  for file in os.listdir(snapshot_dir):
  if file.endswith(".caffemodel"):
  basename = os.path.splitext(file)[0]
  iter = int(basename.split("{}_iter_".format(model_name))[1])
  if iter > max_iter:
   max_iter = iter