Small and Large Stones of Programming

Recently, a href="http://www.vision.caltech.edu/Image_Datasets/CaltechPedestrians/" target="_blank">Caltech pedestrian dataset is often used as a benchmark for Computer Vision. However, this dataset is in an extraordinary format, and so it is not easy to handle it. You can handle it easier by using Matlab, but it is troublesome if you intend to convert it for Python for the sake of, for example, deep learning. I developed conversion tools, so I publish them here.

GPU memory is limited. Although NVIDIA TITAN X, which has 12-GB memory, is expensive, sometimes 12 GB is not sufficient. GPU memory is smaller for subordinate GPU models. A numerical value that can be stored in one byte can be stored as an 8-bit integer in GPU memory, and thus the GPU memory is more effectively used. So this article describes a method to do so.

The Caltech pedestrian dataset contains a special format videos. The following program converts them to normal video format files. The extension of the generated file is ".avi", but ".mpg" can also be used (can be rewritten).