“Image recognition” is the training of computers to recognize objects in images. When you think of image recognition, do you think of hard-to-use software running complicated algorithms with thousands of lines of code? I know I do. But in reality, open-source datasets and free apps have made it easier than ever to dive right into image recognition. Here are a few ideas for image recognition-related projects and activities you can try!
1. Build an image recognition iOS app.
Xcode (Apple’s free development environment for creating iOS, macOS, watchOS, and tvOS apps) introduced CreateML in 2018. The framework was designed to allow Apple developers to easily build machine learning models for use in their apps. Although Xcode and its CoreML framework are capable of integrating ML models built using more specialized software, CreateML’s greatest asset is its native, easy-to-use, drag-and-drop interface which trains, tests, and selects appropriate ML classifiers automatically.
The basic steps for building an image recognition iOS app are:
2. Play an image recognition game.
There are a few fantastic image recognition-related web apps. One of them is Google’s Quick, Draw! game, which gives you something to draw, then guesses what you’re drawing. Another is Google’s Autodraw experiment, in which the computer attempts to complete your drawing.
Another of Google’s AI experiments, Handwriting with a Neural Net, generates strokes matching the style of your handwriting samples, while Cartoonify creates cartoons out of your drawings.
One thing to keep in mind when playing these games is you’re providing your data (your doodles) to Google to improve their software. This may or may not be something you’d want to do; in any similar situation, it’s always a good idea to consider both privacy issues and possible issues with the research and future software you may be enabling (e.g. facial recognition technology).
3. Conduct image recognition research.
Another idea for image recognition-related projects you can try is research, in which you identify a problem, develop a hypothesis, follow a procedure (collect and analyze data), and come to a conclusion regarding the problem you posed previously. Research projects typically last several months.
This may seem a little more daunting than the other two, but one thing you can try is brainstorming ideas for research you might be interested in. There are lots of fantastic datasets out there; I’ve listed a few below that you might find helpful for developing your ideas!
Skin (e.g. moles and lesions): https://isic-archive.com
Handwritten digits: http://yann.lecun.com/exdb/mnist/
Remember: these are just a few examples of well-constructed datasets you can use for your image recognition research. If you’re interested in any topics that aren’t listed above, you can check out a number of sites dedicated to machine learning research and datasets--Kaggle, for example, has a huge inventory of information, datasets, and even competitions dedicated to machine learning.
Which of these image recognition activities do you plan to try? Let us know in the comments! And in the meantime, happy coding!
Photo credit: Artem Kniaz
When you think of technology as an abstract concept, it’s common to associate the term with extraterrestrial spaceships or sentient machines. Technology, however, actually involves the progression of humanity through the development of certain skills, products, et cetera. We tend to restrict our perception of technology to utopian societies or Orwellian dystopias because that is the path that we assume that human development will take us. Sometimes, however, technology can be used to look into the past, which can reveal much more about the human race than we previously predicted.
Archaeological sites, buried under sediment and time, are one of the pieces of history that researchers are looking into. Through her platform GlobalXplorer, Sarah Parcak has revolutionized the way that we look at studying the past. GlobalXplorer accumulates millions of satellite images online so that viewers can peruse the images and identify archaeological sites that may have previously gone unnoticed. According to National Geographic, the project has contributed to the identification of key archaeological structures in Peru and will soon be moved to India, where its past successes are sure to provide key insights. A core concept of Parcak’s model is that it involves volunteers to a great extent. Rather than limiting the ability to make contributions to researchers, GlobalXplorer has provided a platform for people across the globe to piece together the scattered remains of human history.