We apply convolutional neural networks (CNN) to the problem of image orientation detection in the context of determining the correct orientation (from 0, 90, 180, and 270 degrees) of a consumer photo. The problem is especially important for digitazing analog photographs. We substantially improve on the published state of the art in terms of the performance on one of the standard datasets, and test our system on a more difficult large dataset of consumer photos. We use Guided Backpropagation to obtain insights into how our CNN detects photo orientation, and to explain its mistakes.

翻译：我们将卷积神经网络（CNN）应用于消费者照片的正确方向检测问题（从0°、90°、180°和270°角度中确定）。该问题对于模拟照片数字化尤为重要。我们在一个标准数据集上的性能显著超越了现有文献公布的最优水平，并在更具挑战性的消费者照片大型数据集上测试了系统性能。通过引导反向传播方法，我们深入探究了CNN检测照片方向的机制，并解释了其分类错误的原因。