Question

我正在使用dlib的 train_object_detector 进行人脸检测，我在一个文件夹中有大约6k个图像，我正在尝试训练我的模型。

另外，我正在使用dlib的示例python代码（train_object_detector.py）。

但问题是，该程序的RAM使用情况是疯狂的。对于大约300张图像，它需要大约15GB RAM，而现在我的6k图像，我被卡住了。

对于6k图像，在训练时，它需要超过100GB的RAM ，最终程序自行终止。

总是这样吗？或者我做错了什么？拥有这么多RAM使用是否正常？

几乎没有修改，几乎与dlib的示例代码相同。

注意：图像的大小介于10-100 KB之间。

以下是我正在使用的代码（远程）：http://pastebin.com/WipU8qgq 这是代码：

import os
import sys
import glob
import dlib
from skimage import io


if len(sys.argv) != 4:
        print(
        "Give the path to the faces directory as the argument to this "
        "program with training and test xml files in order. For example: \n"
        "    ./train_object_detector_modified.py ../faces ../faces/training.xml ../faces/testing.xml")
    exit()
faces_folder = sys.argv[1]
training_xml_path = sys.argv[2]
testing_xml_path = sys.argv[3]

options = dlib.simple_object_detector_training_options()
options.add_left_right_image_flips = True
options.C = 5
options.num_threads = 8
options.be_verbose = True

dlib.train_simple_object_detector(training_xml_path, "detector.svm", options)
print 'training end'

print("")  # Print blank line to create gap from previous output
print("Training accuracy: {}".format(
    dlib.test_simple_object_detector(training_xml_path, "detector.svm")))

print("Testing accuracy: {}".format(
    dlib.test_simple_object_detector(testing_xml_path, "detector.svm")))


'''
# Now let's use the detector as you would in a normal application.  First we
# will load it from disk.
detector = dlib.simple_object_detector("detector.svm")

# We can look at the HOG filter we learned.  It should look like a face.  Neat!
win_det = dlib.image_window()
win_det.set_image(detector)

# Now let's run the detector over the images in the faces folder and display the
# results.
print("Showing detections on the images in the faces folder...")
win = dlib.image_window()
for f in glob.glob(os.path.join(faces_folder, "*.jpg")):
   print("Processing file: {}".format(f))
   img = io.imread(f)
   dets = detector(img)
   print("Number of faces detected: {}".format(len(dets)))
   for k, d in enumerate(dets):
       print("Detection {}: Left: {} Top: {} Right: {} Bottom: {}".format(
           k, d.left(), d.top(), d.right(), d.bottom()))

   win.clear_overlay()
   win.set_image(img)
   win.add_overlay(dets)
   dlib.hit_enter_to_continue()
'''

Answer 1

这种情况正在发生，因为您有大图像和/或小边框的组合。默认情况下，dlib.train_simple_object_detector使用大小为6400像素的检测窗口。如果图像包含远小于此的目标框，则对这些图像进行上采样以使对象足够大。

所有这些设置都是选项对象中的字段。

dlib train_object_detector大量的RAM使用量

1 个答案: