TensorFlow in version 2.x adopted Keras as part of its libraries. In the past, these two were separate projects. In TensorFlow 1.x, we need to build a computation graph, set up a session, and derive gradients from a session for the deep learning model. Hence it is a bit too verbose. Keras is designed as a library to hide all these low-level details.

As a photographer, it’s important to know the difference between camera sensor sizes, particularly if you’re planning on buying a new camera. Sensor size is the first and most important thing you need to consider. It is the main feature of your camera that will have the most powerful impact on your images.

The APS-C or crop-sensor format is the most well-known and most versatile of the bunch. The APS-C sensor is popular in DSLR and mirrorless cameras alike. Beginners and professionals alike use it thanks to its adaotability.

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The other obsoleted library is Theano. It has ceased development, but once upon a time, it was a major library for deep learning. In fact, the earlier version of the Keras library allows you to choose between a Theano or TensorFlow backend. Indeed, neither Theano nor TensorFlow are deep learning libraries precisely. Rather, they are tensor libraries that make matrix operations and differentiation handy, upon which deep learning operations can be built. Hence these two are considered replacements for each other from Keras’s perspective.

PyTorch is backed by Facebook, and its syntax has been stable over the years. There are also a lot of existing models that we can borrow. The common way of defining a deep learning model in PyTorch is to create a class:

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One major difference between PyTorch and Keras syntax is in the training loop. In Keras, we just need to assign the loss function, the optimization algorithm, the dataset, and some other parameters to the model. Then we have a fit() function to do all the training work, as follows:

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Full-frame sensors are available in both DSLR and mirrorless cameras. They have the same dimensions as the 35mm film, hence the name. The 35mm full-frame sensor type is the gold standard among professional photographers who want the highest-quality images.

Again, there is no simple yes or no answer to this question. In the last decade or so, CMOS sensors have become a lot more prevalent than CCD sensors. Most consumers cameras and cell phones manufactured today use CMOS sensors. CMOS sensors, in general, use less power, therefore the camera battery will last longer.

Any sensor that is about 1.5 to 1-inch in size or smaller can be found in non-interchangeable lens cameras (your typical point and shoot) and smartphone cameras.

Also, note that the libraries mentioned above are full-featured libraries that include training and prediction. If you consider a production environment where you make use of a trained model, there could be a wider choice. TensorFlow has a “TensorFlow Lite” counterpart that allows a trained model to be run on a mobile or the web. Intel also has an OpenVINO library that aims to optimize the performance in prediction.

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Deep learning has gained attention in the last decade. Before that, there was little confidence in how to train a neural network with many layers. However, understanding how to build a multilayer perceptron was around for many years.

You raise some interesting questions Romeo! The answers depend upon specific goals (i.e. research, product development, education…etc).

Chainer is another library in Python. It is an influential one because the syntax makes a lot of sense. While it is less common nowadays, the API in Keras and PyTorch bears a resemblance to Chainer. The following is an example from Chainer’s documentation, and you may mistake it as Keras or PyTorch:

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The answer to this question isn’t a simple yes or no. It all depends on what’s most important to you. In general, the larger the sensor the better the image quality because it can acquire more light, generates less noise, and can create a shallower depth of field (more background blur) which is preferred my many for portraiture work.

PyTorch and TensorFlow are the two major libraries nowadays. In the past, when TensorFlow was in version 1.x, they were vastly different. But as TensorFlow absorbed Keras as part of its library, these two libraries mostly work similarly.

High-end compact cameras like the Panasonic Lumix DMC-LX10 and the Sony Cyber-Shot DSC-RX10 IV use 1-inch sensors. This allows these cameras to produce good results—in terms of image and video quality—that you won’t get with regular point-and-shoot cameras.

Keep in mind that camera sensor formats are not standardized across the different brands or models. Dimensions may vary slightly from the figures listed above.

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The typical APS-C sensor size is different across camera brands. Canon APS-C sensors are usually 22.3×14.9mm, while other brands like Nikon, Sony, Pentax, and more usually feature APS-C sensors with 23.6×15.6mm dimensions. Many cameras including the Canon EOS M50 Mark II, Fujifilm X100V, Sony Alpha a6600, and Nikon Z50 all hold APS-C sensors.

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CNTK from Microsoft and Apache MXNet are the two other libraries worth mentioning. They are large with interfaces for multiple languages. Python, of course, is one of them. CNTK has C# and C++ interfaces, while MXNet provides interfaces for Java, Scala, R, Julia, C++, Clojure, and Perl. But recently, Microsoft decided to stop developing CNTK. But MXNet does have some momentum, and it is probably the most popular library after TensorFlow and PyTorch.

Due to their large image sensors, medium-format cameras are traditionally heavier and bulkier than their full-frame counterparts. But that changed, as brands like Hasselblad have come out with smaller mirrorless medium-format cameras like the X1D II to provide photographers with a lighter, more compact option. The newer Fujifilm GFX 100 is also a medium-format mirrorless camera and holds a whopping 102MP resolution.

The APS-H is slightly larger than the APS-C sensor format that many Canon DSLR cameras use today but smaller than a traditional full-frame sensor.

Medium format is the largest sensor type in digital cameras for photographic applications. However, it doesn’t come in just one size. Medium format has its own group of sensors, with its own equivalents to the four thirds, APS-C, and full-frame formats. There are a variety of sensor sizes for medium-format cameras, and typical sizes range from around 43.8×32.9mm to 53.7×40.2mm.

Not all cameras are created equal. An entry-level DSLR won’t give you the same results from a professional, full-frame DSLR—even if they have exactly the same pixel count. If you want to get the highest-quality images with your camera, you’ll need something with extremely powerful specifications and a physically large image sensor.

The groundbreaking EOS-1D was the first Canon camera to carry the APS-H sensor type was, and it launched in 2001. Canon released four more cameras (all members of the 1D line) with the same sensor type before discontinuing it.

Created by Olympus and Panasonic, the Four Thirds System is a standard that allows for the compatibility of lenses and bodies across participating camera makers. Image sensor size is 17.3×13mm with a crop factor of 2.0 when compared to full-frame camera sensors.

There’s also the medium-format cameras—the lesser known of the group. These cameras have the largest sensors out of all the available digital cameras for photography, which means they can get pretty expensive.

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However, a smaller sensor allows added reach (zoom). For example, on a micro 4/3 sensor, which is has a crop factor of two compared to a full frame sensor, a 200mm lens becomes the equivalent of a 400mm lens. Smaller sensors also allow for an overall more compact camera and lens system, which is convenient for travel and long hikes. Finally, smaller sensor cameras are generally less expensive.

Before we had deep learning, probably the most famous neural network library was libann. It is a library for C++, and the functionality is limited due to its age. This library has since stopped development. A newer library for C++ is OpenNN, which allows modern C++ syntax.

One of the earliest libraries for deep learning is Caffe. It was developed at U.C. Berkeley specifically for computer vision problems. While it is developed in C++, it serves as a library with a Python interface. Hence we can build our project in Python with the network defined in a JSON-like syntax.

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As an additional layer of complexity, each bucket has a filter on it that only lets in red, green, or blue light. In essence, each bucket can only collect 1/3 of the total light trying to enter it. For each bucket, the amount of the other colors is approximated. All this information is then converted to the final image you see on your screen.

On the mirrorless camera side, we have the Micro Thirds Format System, first released in 2008. It shares the Four Thirds System’s sensor size and specifications but uses a compact design with no space for the movable mirror, pentaprism, and other parts of the DSLR mechanisms not found in mirrorless cameras.

We’ve all heard of the full-frame DSLR camera, of course, which is the gear of choice of seasoned professional photographers. For enthusiasts and beginners, the usual choice is the APS-C format or crop-sensor DSLR camera. However, some prefer to use mirrorless cameras or MILCs, which are the smaller, lighter versions of DSLRs. Lastly, there are the 1-inch sensor cameras, which are better known as point-and-shoot or compact digital cameras.

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Essentially, a sensor is made up of tiny individual photosites. Think of each photosite as a bucket covered by a lid. When an exposure is initiated (press of the shutter button), the lid is uncovered to collect photons of light. When the exposure stops, the lid is placed back on the buckets (photosites). The collected photons are then converted to electrical signal, and the strength of that signal is determined by how many total photons were collected.

Many digital cameras are commercially available on the market right now, and they all have a wide range of sensor sizes. And while it’s good to have choices, it can also get pretty confusing, especially to a beginner.

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Meanwhile, CCD sensors tend to produce less noise which translates to images appearing sharper. This goes hand in hand with CCD sensors being more sensitive in lower light conditions. Because CMOS sensors are much more available and costs less to manufacture than CCD sensors, cameras with CMOS sensors are usually less expensive.

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Note that both PyTorch and TensorFlow are libraries with a Python interface. Therefore, it is possible to have an interface for other languages too. For example, there are Torch for R and TensorFlow for R.

In this post, you discovered various deep learning libraries and some of their characteristics. Specifically, you learned:

A camera’s sensor dictates the quality of the images it can produce—the larger the sensor, the higher the image quality. Bigger image sensors have bigger pixels, which means better low-light performance, reduced noise, good dynamic range, and the ability to obtain more information.

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Machine learning is a broad topic. Deep learning, in particular, is a way of using neural networks for machine learning. A neural network is probably a concept older than machine learning, dating back to the 1950s. Unsurprisingly, there were many libraries created for it.

But that’s pretty much all for C++. The rigid syntax of C++ may be why we do not have too many libraries for deep learning. The training phase of a deep learning project is about experiments. We want some tools that allow us to iterate faster. Hence a dynamic programming language could be a better fit. Therefore, you see Python come on the scene.

Below is an example of using MXNet via the R interface. Conceptually, you see the syntax is similar to Keras’s functional API:

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The Four Thirds System uses a 4:3 image aspect ratio, hence the name, and is featured in cameras like the Blackmagic Design Pocket Cinema Camera 4K. The Micro Four Thirds System uses the same ratio but can also record 16:9, 3:2, and 1:1 formats. It is included in cameras like the Olympus OM-D E-M1 Mark III and Panasonic Lumix G9.

This may not be an issue if you’re experimenting with a new design of a network in which you want to have more control over how the loss is calculated and how the optimizer updates the model weights. But otherwise, you will appreciate the simpler syntax from Keras.

Rapidly these libraries are moving away from academic institutions to being backed by commercial companies. Eventually this left me wondering that as deep learning moves from research to production do we expect a need for more C++ and java libraries for machine learning? Versus maybe if there were more C++ and java libraries, would more companies would be picking up machine learning solutions? Overall, I’m looking forward to looking into more of those production environment libraries you mentioned. –Romeo

The Canon EOS R5, for example, is a full-frame mirrorless camera option, and the popular Nikon D850 DSLR has a FX full-frame sensor.