Google Lens is not just for flowers and famous buildings. I aimed it at one of the most disordered parts of my home — my junk drawer — and asked it to name five random bits and bobs in there. The results were from uncannily accurate to hilariously off base, and in the process I learned a few things about how visual search actually works.
Google says Lens is used more than 10 billion times a month, suggesting that photo-based search has become a habit akin to searching the web. But can it handle specialty maker parts and medical-grade thingamajigs with virtually no context? I volunteered to try it so you don’t have to.
How I tested Google Lens in my home
I selected five mysteries from a decade of tech projects: flexible 3D-printing parts, a spring-loaded lancet dating back to the last century and one rectangular perforated scrap that I knew had a name but couldn’t recall what it was.
I opened Lens using the Google app on an iPhone, gave it access to my photos and shot each item on a clean surface in good light. On Android, the process is almost same.
I made a number of photos of each item, from several angles, some extreme close-ups and crops to minimize background distractions. When a label or logo arose, I snapped a secondary frame to see had lettering recognition thus far lead me.
What Lens nailed
First up: a transparent, fleshlike sleeve I couldn’t ID. Lens was a nice fit to an Ultimaker nozzle cover. It’s a very particular enclosure for protecting the hot-end area on some Ultimaker 3D printers —quite a “deep cut” considering how many off-brand carriers there are out there.
Then followed a plastic tool stamped with “Anycubic.” Instead of seeing only the brand echoed, Lens spotted the feeler gauge used to measure a gap between a nozzle and build plate on a 3D printer. That’s right on the nose — and just the sort of immediate, useful response that makes visual search feel like magic.
That was followed by a black silicone number that resembled a bizarre oven mitt for ants. Lens pinned down that it was a thermal sock for the hotend of the Creality K1 Max even mentioning compatible variations. Hotends can reach temperatures of 300°C (over half the melting point of standard filament!).These socks reduce and insulate any filament residue that may be baked on to the heater block! The brand-level specificity was just right.
Where Lens stumbled
A thin, spring-loaded tube first set Lens off into the direction of “wood shaker leg” — in essence a piece of furniture.
After I took off a protective cap and shot again, it flipped correctly to “lancet,” which is a common device used for rapid blood work. In this maker space, lancets also double as nifty uncloggers of filament tubing and nozzles. The reversal illustrates how small adjustments, in this case a bit more exposure of the mechanism, can alter different outcomes.
The last mystery was a bendable rectangle with a grid of small posts. Lens even recommended products as varied as gardening deterrents and wildlife spikes. The correct response: nozzle wiper for a Lulzbot Mini 3D printer, it’s for cleaning wayward filament off the nozzle right before a print. It was a reasonable failure — when there’s no scale, or familiar context, a rubber wiper can easily be mistaken for a much larger outdoor product.
Why visual search stumbles around scale & context
The most sophisticated image models can’t inherently know size from just one picture. They make inferences from textures and shapes to context. Work from groups like MIT CSAIL has shown that recognition systems are sensitive to scale and background cues; resize the size or crop and your match set can change radically. That’s the case with the “spikes” idea — structural similarity triumphs over worldly dimensions.
Lens also draws on the Knowledge Graph and a large corpus of shopping and product images. That’s why it works so well with branded props and accessories, where there is an abundance of labeled data. Niche components with no clear marking or recognized listing linger in the long tail: Where games are rarer, and errors more common.
Google has been promoting multimodal technologies such as multisearch and scene understanding that fuse text prompts with images for more context. Good progress, but size ambiguity is just a classic 2D recognition problem unless you provide a reference object.
Pro tips for improving Google Lens accuracy
Shoot on a plain, high-contrast background and avoid clutter that can overwhelm the match.
Put an everyday reference object for scale — a coin, or a pen — just off center to keep Lens in focus on your target but infer size.
Shoot at different angles and crop close to the object. If there is a removable cap or sleeve, snap one photo with it and one without.
Use text mode in Lens when you can see any kind of label or logo, partial or not — OCR may be able to steer the search towards the proper family of product.”
To compare results, try “Search image with Google” in Chrome on the desktop, then refine using different crops.
The takeaway
Out of five oddballs, Lens nailed three identifications, corrected one on reconsideration and missed the last because he was confused about scale. That’s a solid showing for the messy realities of a junk drawer. Visual search is already a great labor-saving tool for makers, tinkerers, and anyone who spends a long time staring at a thing to no avail — now with Spots it’s that much more powerful so long as you have some decent photos of the part or parts you need.