MobileNet - Allowing Mobile Devices to See (2017)

David Landup
David Landup

At this point - computer vision was catching quite a bit of wind. Models were getting more and more efficient in terms of parameters, and enough practical experience and theoretical knowledge was spread amongst a large population that further optimizations could take place. Just as computers were bulky and inefficient, so were computer vision networks. As computers became smaller - we put them in our pockets. In 2017, Andrew Howard et al. decided to put computer vision into our pockets as well.

In "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications", the authors note that while networks are getting more efficient and powerful, they're also getting larger. While this works great for tasks that aren't time-sensitive, other applications that are time sensitive couldn't rely on fast inference. To address the need to let smaller devices such as mobile phones and embedded devices see - MobileNet was created. The key takeaways are that MobileNet is small (easy to upload, download, load into memory and use) and fast (fast inference is required for mobile devices, where every millisecond of delay is felt). In real-time scenarios, being fast trumps being accurate, and "good enough" solutions are much more acceptable.

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