使用> 450K实例来跟踪Dlib对象检测器 [英] Traning Dlib object detector with >450K instances

问题描述

dlib是否具有用于训练目标检测器的大规模数据集。我有> 450K的人脸图像来训练人脸检测器。可以使用Dlib还是我需要定向到另一个替代方法？

Is dlib capable of large scale datasets for training object detector. I have >450K face images to train a face detector. Is it possible to use Dlib or I need to direct to another alternative?

推荐答案

您可以使用多少数据是一个函数？您的计算机中有多少RAM。因此，也许您可​​以根据每个图像的大小以及您拥有的RAM数量来进行训练。

How much data you can use is a function of how much RAM is in your computer. So maybe you can train on that many depending on how large each image is and how much RAM you have.

但是更重要的是，您可能会询问HOG + SVM dlib中的检测器。对于训练面部检测器，450K张面部远远超出了HOG + SVM的收益递减点。例如，dlib附带的正面人脸检测器非常准确，仅在一个小的62MB数据集上进行训练（该数据集 http://dlib.net/files/data/dlib_face_detector_training_data.tar.gz ）。用数千张图像训练这种检测器不会为您带来任何额外的准确性。

But more importantly, you are probably asking about the HOG+SVM detector in dlib. And for training a face detector, 450K faces is far beyond the point of diminishing returns for HOG+SVM. For example, the frontal face detector that comes with dlib, which is very accurate, is trained on only a small 62MB dataset (this one http://dlib.net/files/data/dlib_face_detector_training_data.tar.gz). Training this kind of detector with more than a few thousand images is not going to get you any additional accuracy.

现在，如果您的数据中存在很多姿态变化那么HOG + SVM将无法捕获它。在这种情况下，最好的办法是训练多个探测器，每个姿势一个。您可以使用dlib的imglab工具的--cluster选项将数据集自动聚类为不同的姿势。

Now if you have a whole lot of pose variability in your data then HOG+SVM isn't going to be able to capture that. The best thing to do in that case is to train multiple detectors, one for each pose. You can automatically cluster your dataset into different poses using the --cluster option of dlib's imglab tool.

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