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The following code unpickles each of the five batch files and loads all of the images into a NumPy array: It also has a potentially serious disadvantage of posing a security risk and not coping well when dealing with very large quantities of data. While we won’t consider pickle or cPickle in this article, other than to extract the CIFAR dataset, it’s worth mentioning that the Python pickle module has the key advantage of being able to serialize any Python object without any extra code or transformation on your part.

They have actually been serialized and saved in batches using cPickle.

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When you download and unzip the folder, you’ll discover that the files are not human-readable image files. You’ll be sacrificing 163MB of disk space: Image: A. If you’d like to follow along with the code examples in this article, you can download CIFAR-10 here, selecting the Python version.

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Relatively, CIFAR is not a very large dataset, but if we were to use the full TinyImages dataset, then you would need about 400GB of free disk space, which would probably be a limiting factor.Ĭredits for the dataset as described in chapter 3 of this tech report go to Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton. We will be using the Canadian Institute for Advanced Research image dataset, better known as CIFAR-10, which consists of 60,000 32x32 pixel color images belonging to different object classes, such as dogs, cats, and airplanes. If none of the storage methods ring a bell, don’t worry: for this article, all you need is a reasonably solid foundation in Python and a basic understanding of images (that they are really composed of multi-dimensional arrays of numbers) and relative memory, such as the difference between 10MB and 10GB.

How the three methods compare in terms of disk usage.

What the performance differences are when you’re reading and writing many images.

What the performance differences are when you’re reading and writing single images.

Why alternate storage methods are worth considering.

Storing images in hierarchical data format (HDF5).

Storing images in lightning memory-mapped databases (LMDB).

Keep reading, and you’ll be convinced that it would take quite awhile-at least long enough to leave your computer and do many other things while you wish you worked at Google or NVIDIA. Think about how long it would take to load all of them into memory for training, in batches, perhaps hundreds or thousands of times. ImageNet is a well-known public image database put together for training models on tasks like object classification, detection, and segmentation, and it consists of over 14 million images. If you’re interested, you can read more about how convnets can be used for ranking selfies or for sentiment analysis. Algorithms like convolutional neural networks, also known as convnets or CNNs, can handle enormous datasets of images and even learn from them. Increasingly, however, the number of images required for a given task is getting larger and larger. jpg files, is both suitable and appropriate. Even if you’re using the Python Imaging Library (PIL) to draw on a few hundred photos, you still don’t need to. Why would you want to know more about different ways of storing and accessing images in Python? If you’re segmenting a handful of images by color or detecting faces one by one using OpenCV, then you don’t need to worry about it.

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