D-lib对象检测器培训
我正在尝试使用D-lib训练对象检测器。我选择了近100张图像进行训练。我正在使用Python环境。根据文档，我使用Imglab工具在图像上绘制边界框。每个图像的大小几乎为4000 * 3000像素。
然后将生成的XML文件放入我的位置，并调用检测程序。这是我的疑问和疑问。
I am trying to train an object detector using D-lib. I selected close to 100 images for training. I am using the Python environment. As per documentation, I used the Imglab tool to draw the bounding boxes across the images. Every image is almost 4000*3000 pixels in size. And then placed the generated XML file into my location and called the detector program. Here are my doubts and questions.
在运行程序时，我应该使用什么作为测试XML文件？
我在没有分配任何测试XML的情况下运行。但是，我得到以下信息。 C训练：5 epsilon训练：0.01 8线训练。使用81像素宽x 79像素高的滑动窗口进行训练。左右翻转图像的训练。被杀死的
被杀死是什么意思？
What should I use as the testing XML file while running the program? I ran without assigning any testing XML in place. However, I get the following. Training with C: 5 Training with epsilon: 0.01 Training using 8 threads. Training with sliding window 81 pixels wide by 79 pixels tall. Training on both left and right flipped versions of images. Killed What does 'killed' mean by the way?
我现在该怎么办？
测试XML文件提供图像和对象批注，用于检查对象检测的准确性。可以使用imglab以及培训XML来生成文件。它应包含与训练数据集相似但不相同的数据。
The testing XML file gives images and object annotations to be used to check the accuracy of your object detection. The file can be generated using imglab as with the training XML. It should contain data that is similar to, but not identical to, your training dataset.
Killed 消息为由于内存问题。在Linux中，这是OOM Killer的结果。由于图像太大，因此您的计算机内存不足，并终止了培训过程。
可能的解决方案：
1）配置OOM杀手以允许dlib使用更多的内存。
2）如果可能，请缩小图像的大小。
3）使用具有更多RAM的计算机。如果您手边没有，则AWS会以相对较高的价格提供几种高RAM的 EC2选项低成本。
The Killed message is due to memory issues. In Linux, it is the result of the OOM Killer. Since the images are so large, your machine is running out of memory and killing the training process.
Possible solutions:
1) Configure the OOM killer to allow dlib to use more memory.
2) Scale down the images if possible for your application.
3) Use a machine with more RAM. If you don't have one on hand, AWS offers several high-RAM EC2 options at relatively low cost.