Computer Vision

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Computer vision researchers build an AI benchmark app for Android phones

A group of computer vision researchers from ETH Zurich want to do their bit to enhance AI development on smartphones. To wit: They’ve created a benchmark system for assessing the performance of several major neural network architectures used for common AI tasks.

They’re hoping it will be useful to other AI researchers but also to chipmakers (by helping them get competitive insights); Android developers (to see how fast their AI models will run on different devices); and, well, to phone nerds — such as by showing whether or not a particular device contains the necessary drivers for AI accelerators. (And, therefore, whether or not they should believe a company’s marketing messages.)

The app, called AI Benchmark, is available for download on Google Play and can run on any device with Android 4.1 or higher — generating a score the researchers describe as a “final verdict” of the device’s AI performance.

AI tasks being assessed by their benchmark system include image classification, face recognition, image deblurring, image super-resolution, photo enhancement or segmentation.

They are even testing some algorithms used in autonomous driving systems, though there’s not really any practical purpose for doing that at this point. Not yet anyway. (Looking down the road, the researchers say it’s not clear what hardware platform will be used for autonomous driving — and they suggest it’s “quite possible” mobile processors will, in future, become fast enough to be used for this task. So they’re at least prepped for that possibility.)

The app also includes visualizations of the algorithms’ output to help users assess the results and get a feel for the current state-of-the-art in various AI fields.

The researchers hope their score will become a universally accepted metric — similar to DxOMark that is used for evaluating camera performance — and all algorithms included in the benchmark are open source. The current ranking of different smartphones and mobile processors is available on the project’s webpage.

The benchmark system and app was around three months in development, says AI researcher and developer Andrey Ignatov.

He explains that the score being displayed reflects two main aspects: The SoC’s speed and available RAM.

“Let’s consider two devices: one with a score of 6000 and one with a score of 200. If some AI algorithm will run on the first device for 5 seconds, then this means that on the second device this will take about 30 times longer, i.e. almost 2.5 minutes. And if we are thinking about applications like face recognition this is not just about the speed, but about the applicability of the approach: Nobody will wait 10 seconds till their phone will be trying to recognize them.

“The same is about memory: The larger is the network/input image — the more RAM is needed to process it. If the phone has a small amount of RAM that is e.g. only enough to enhance 0.3MP photo, then this enhancement will be clearly useless, but if it can do the same job for Full HD images — this opens up much wider possibilities. So, basically the higher score — the more complex algorithms can be used / larger images can be processed / it will take less time to do this.”

Discussing the idea for the benchmark, Ignatov says the lab is “tightly bound” to both research and industry — so “at some point we became curious about what are the limitations of running the recent AI algorithms on smartphones”.

“Since there was no information about this (currently, all AI algorithms are running remotely on the servers, not on your device, except for some built-in apps integrated in phone’s firmware), we decided to develop our own tool that will clearly show the performance and capabilities of each device,” he adds. 

“We can say that we are quite satisfied with the obtained results — despite all current problems, the industry is clearly moving towards using AI on smartphones, and we also hope that our efforts will help to accelerate this movement and give some useful information for other members participating in this development.”

After building the benchmarking system and collating scores on a bunch of Android devices, Ignatov sums up the current situation of AI on smartphones as “both interesting and absurd”.

For example, the team found that devices running Qualcomm chips weren’t the clear winners they’d imagined — i.e. based on the company’s promotional materials about Snapdragon’s 845 AI capabilities and 8x performance acceleration.

“It turned out that this acceleration is available only for ‘quantized’ networks that currently cannot be deployed on the phones, thus for ‘normal’ networks you won’t get any acceleration at all,” he says. “The saddest thing is that actually they can theoretically provide acceleration for the latter networks too, but they just haven’t implemented the appropriated drivers yet, and the only possible way to get this acceleration now is to use Snapdragon’s proprietary SDK available for their own processors only. As a result — if you are developing an app that is using AI, you won’t get any acceleration on Snapdragon’s SoCs, unless you are developing it for their processors only.”

Whereas the researchers found that Huawei’s Kirin’s 970 CPU — which is technically even slower than Snapdragon 636 — offered a surprisingly strong performance.

“Their integrated NPU gives almost 10x acceleration for Neural Networks, and thus even the most powerful phone CPUs and GPUs can’t compete with it,” says Ignatov. “Additionally, Huawei P20/P20 Pro are the only smartphones on the market running Android 8.1 that are currently providing AI acceleration, all other phones will get this support only in Android 9 or later.”

It’s not all great news for Huawei phone owners, though, as Ignatov says the NPU doesn’t provide acceleration for ‘quantized’ networks (though he notes the company has promised to add this support by the end of this year); and also it uses its own RAM — which is “quite limited” in size, and therefore you “can’t process large images with it”…

“We would say that if they solve these two issues — most likely nobody will be able to compete with them within the following year(s),” he suggests, though he also emphasizes that this assessment only refers to the one SoC, noting that Huawei’s processors don’t have the NPU module.

For Samsung processors, the researchers flag up that all the company’s devices are still running Android 8.0 but AI acceleration is only available starting from Android 8.1 and above. Natch.

They also found CPU performance could “vary quite significantly” — up to 50% on the same Samsung device — because of throttling and power optimization logic. Which would then have a knock on impact on AI performance.

For Mediatek, the researchers found the chipmaker is providing acceleration for both ‘quantized’ and ‘normal’ networks — which means it can reach the performance of “top CPUs”.

But, on the flip side, Ignatov calls out the company’s slogan — that it’s “Leading the Edge-AI Technology Revolution” — dubbing it “nothing more than their dream”, and adding: “Even the aforementioned Samsung’s latest Exynos CPU can slightly outperform it without using any acceleration at all, not to mention Huawei with its Kirin’s 970 NPU.”

“In summary: Snapdragon — can theoretically provide good results, but are lacking the drivers; Huawei — quite outstanding results now and most probably in the nearest future; Samsung — no acceleration support now (most likely this will change soon since they are now developing their own AI Chip), but powerful CPUs; Mediatek — good results for mid-range devices, but definitely no breakthrough.”

It’s also worth noting that some of the results were obtained on prototype samples, rather than shipped smartphones, so haven’t yet been included in the benchmark table on the team’s website.

“We will wait till the devices with final firmware will come to the market since some changes might still be introduced,” he adds.

For more on the pros and cons of AI-powered smartphone features check out our article from earlier this year.

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‘SmartLens’ app created by a high schooler is a step towards all-purpose visual search

A couple of years ago I was eagerly expectant of an app that would identify anything you pointed it at. Turns out the problem was much harder than anyone expected — but that didn’t stop high school senior Michael Royzen from trying. His app, SmartLens, attempts to solve the problem of seeing something and wanting to identify and learn more about it — with mixed success, to be sure, but it’s something I don’t mind having in my pocket.

Royzen reached out to me a while back and I was curious — as well as skeptical — about the idea that where the likes of Google and Apple have so far failed (or at least failed to release anything good), a high schooler working in his spare time would succeed. I met him at a coffee shop to see the app in action and was pleasantly surprised, but a little baffled.

The idea is simple, of course: You point your phone’s camera at something and the app attempts to identify it using an enormous but highly optimized classification agent trained on tens of millions of images. It connects to Wikipedia and Amazon to let you immediately learn more about what you’ve ID’ed, or buy it.

It recognizes more than 17,000 objects — things like different species of fruit and flower, landmarks, tools and so on. The app had little trouble telling an apple from a (weird-looking) mango, a banana from a plantain and even identified the pistachios I’d ordered as a snack. Later, in my own testing, I found it quite useful for identifying the plants springing up in my neighborhood: periwinkles, anemones, wood sorrel, it got them all, though not without the occasional hesitation.

The kicker is that this all happens offline — it’s not sending an image over the cell network or Wi-Fi to a server somewhere to be analyzed. It all happens on-device and within a second or two. Royzen scraped his own image database from various sources and trained up multiple convolutional neural networks using days of AWS EC2 compute time.

Then there are far more than that number in products that it recognizes by reading the text of the item and querying the Amazon database. It ID’ed books, a bottle of pills and other packaged goods almost instantly, providing links to buy them. Wikipedia links pop up if you’re online as well, though a considerable amount of basic descriptions are kept on the device.

On that note, it must be said that SmartLens is a more than 500-megabyte download. Royzen’s model is huge, since it must keep all the recognition data and offline content right there on the phone. This is a much different approach to the problem than Amazon’s own product recognition engine on the Fire Phone (RIP) or Google Goggles (RIP) or the scan feature in Google Photos (which was pretty useless for things SmartLens reliably did in half a second).

“With the several past generations of smartphones containing desktop-class processors and the advent of native machine learning APIs that can harness them (and GPUs), the hardware exists for a blazing-fast visual search engine,” Royzen wrote in an email. But none of the large companies you would expect to create one has done so. Why?

The app size and toll on the processor is one thing, for sure, but the edge and on-device processing is where all this stuff will go eventually — Royzen is just getting an early start. The likely truth is twofold: it’s hard to make money and the quality of the search isn’t high enough.

It must be said at this point that SmartLens, while smart, is far from infallible. Its suggestions for what an item might be are almost always hilariously wrong for a moment before arriving at, as it often does, the correct answer.

It identified one book I had as “White Whale,” and no, it wasn’t Moby Dick. An actual whale paperweight it decided was a trowel. Many items briefly flashed guesses of “Human being” or “Product design” before getting to a guess with higher confidence. One flowering bush it identified as four or five different plants — including, of course, Human Being. My monitor was a “computer display,” “liquid crystal display,” “computer monitor,” “computer,” “computer screen,” “display device” and more. Game controllers were all “control.” A spatula was a wooden spoon (close enough), with the inexplicable subheading “booby prize.” What?!

This level of performance (and weirdness in general, however entertaining) wouldn’t be tolerated in a standalone product released by Google or Apple. Google Lens was slow and bad, but it’s just an optional feature in a working, useful app. If it put out a visual search app that identified flowers as people, the company would never hear the end of it.

And the other side of it is the monetization aspect. Although it’s theoretically convenient to be able to snap a picture of a book your friend has and instantly order it, it isn’t so much more convenient than taking a picture and searching for it later, or just typing the first few words into Google or Amazon, which will do the rest for you.

Meanwhile for the user there is still confusion. What can it identify? What can’t it identify? What do I need it to identify? It’s meant to ID many things, from dog breeds and storefronts, but it likely won’t identify, for example, a cool Bluetooth speaker or mechanical watch your friend has, or the creator of a painting at a local gallery (some paintings are recognized, though). As I used it I felt like I was only ever going to use it for a handful of tasks in which it had proven itself, like identifying flowers, but would be hesitant to try it on many other things when I might just be frustrated by some unknown incapability or unreliability.

And yet the idea that in the very near future there will not be something just like SmartLens is ridiculous to me. It seems so clearly something we will all take for granted in a few years. And it’ll be on-device, no need to upload your image to a server somewhere to be analyzed on your behalf.

Royzen’s app has its issues, but it works very well in many circumstances and has obvious utility. The idea that you could point your phone at the restaurant you’re across the street from and see Yelp reviews two seconds later — no need to open up a map or type in an address or name — is an extremely natural expansion of existing search paradigms.

“Visual search is still a niche, but my goal is to give people the taste of a future where one app can deliver useful information about anything around them — today,” wrote Royzen. “Still, it’s inevitable that big companies will launch their competing offerings eventually. My strategy is to beat them to market as the first universal visual search app and amass as many users as possible so I can stay ahead (or be acquired).”

My biggest gripe of all, however, is not the functionality of the app, but in how Royzen has decided to monetize it. Users can download it for free but upon opening it are immediately prompted to sign up for a $2/month subscription — before they can even see whether the app works or not. If I didn’t already know what the app did and didn’t do, I would delete it without a second thought upon seeing that dialog, and even knowing what I do, I’m not likely to pay in perpetuity for it.

A one-time fee to activate the app would be more than reasonable, and there’s always the option of referral codes for those Amazon purchases. But demanding rent from users who haven’t even tested the product is a non-starter. I’ve told Royzen my concerns and I hope he reconsiders.

It would also be nice to scan images you’ve already taken, or save images associated with searches. UI improvements like a confidence indicator or some kind of feedback to let you know it’s still working on identification would be nice as well — features that are at least theoretically on the way.

In the end I’m impressed with Royzen’s efforts — when I take a step back it’s amazing to me that it’s possible for a single person, let alone one in high school, to put together an app capable of completing such sophisticated computer vision tasks. It’s the kind of (over-) ambitious app-building one expects to come out of a big, playful company like the Google of a decade ago. This may be more of a curiosity than a tool right now, but so were the first text-based search engines.

SmartLens is in the App Store now — give it a shot.

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Microsoft advances several of its hosted artificial intelligence algorithms

 Microsoft Cognitive Services is home to the company’s hosted artificial intelligence algorithms. Today, the company announced advances to several Cognitive Services tools including Microsoft Custom Vision Service, the Face API and Bing Entity Search . Joseph Sirosh, who leads the Microsoft’s cloud AI efforts, defined Microsoft Cognitive Services in a company blog post announcing… Read More

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SignAll is slowly but surely building a sign language translation platform

 Translating is difficult work, the more so the further two languages are from one another. French-Spanish? Not a problem. Ancient Greek-Esperanto? Hard. But sign language is uniquely difficult because it is fundamentally different from spoken and written languages. All the same, companies like SignAll are working hard to make accurate, real-time machine translation of ASL a reality. Read More

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AWS ramps up in AI with new consultancy services and Rekognition features

 Ahead of Amazon’s AWS division big Re:invent conference next week, the company has announced two developments in the area of artificial intelligence. AWS is opening a machine learning lab, ML Solutions Lab, to pair Amazon machine learning experts with customers looking to build solutions using the AI tech. And it’s releasing news feature within Amazon Rekognition, Amazon’s… Read More

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iUNU aims to build cameras on rails for growers to keep track of their crop health

 You’ve probably spent a lot of time keeping track of your plants and all the minor details, like the coloration of the leaves, in order to make sure they’re healthy — but for professional growers in greenhouses, this means keeping track of thousands of plants all at once. That can get out of hand really quickly as it could involve just walking through a greenhouse with an… Read More

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Goodbye, photo studios. Hello, Colormass virtual photoshoots

 Ikea is a leader among those that have pushed the limits when it comes to using digital imaging to take product marketing to the next level. When you look at an Ikea catalog or its website, you might think you are looking at rooms full of Swedish sofas, coffee tables and stylish lamps, but you’re actually looking at highly realistic, but digitally manipulated 3D facsimiles — the… Read More

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Canvas’ robot cart could change how factories work

We stopped by Andy Rubin’s Playground in Palo Alto to check out a new autonomous cart from Canvas Technologies. The startup aims to replace existing fixed and expensive factory infrastructure, like conveyor belts, with its lightweight and adaptable computer-vision-powered cart. Read More

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After Magic Leap acquisition, Dacuda founder forms PXL Vision

 Swiss startup Dacuda’s 3D scanning division was acquired by Magic Leap in February, but it seems the company’s founder, Michael Born, is just getting started with computer vision. He and three others who didn’t go over to the much-hyped and intensely secretive AR company have formed a new one: PXL Vision AG, focused on real-time image classification and Read More

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