Comment by dlau | Hacker News Reader

Comment by dlau | Hacker News Reader

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After trialling several image recognition and categorization API services, Watson was by far the least impressive when you use their default classifier.

For a project I've been building, I have used Clarif.ai, Google Vision API, Watson, and Imagga. From empirical tests, Watson has always provided poor, if not hilariously non-sensical, results. As an example, I use this image of a baby in a stroller (https://dl.dropboxusercontent.com/u/898689/IMG_2948.jpg). Here are the results in classification tags from the 4 services:

Clarifai: people, child, vehicle, woman, one, man, outdoors, emergency, accident, protest, adult, transportation system, carriage, portrait, wheelchair, safety, road, bike, leisure, wheel

Google: baby carriage, car seat, child, vehicle, diving equipment

Watson: performing, escalator, repairing, indoors, celebration, dancing, human, amusement arcade, bottle, baggage claim, group of people, appliance, tiger, people, big group, child, mixed color

Imagga: people portraits

These results obviously may vary depending on image subject, composition, etc., but I've basically dismissed Watson as a viable off-the-shelf visual recognition API. That being said, if you have a specific dataset of images that positively and negatively identify a concept, Watson's custom classifiers may be of interest although I haven't tried that.

Chris from Imagga here. Just tested with the provided image and the demo returns the following tags: child, happy, kid, cute, person, caucasian, little, boy, people, happiness, childhood, smile, fun, portrait, smiling, pedestrian, children, outside, etc. We are less conservative with keywords we return and usually offer way more than just two words, that made me check what the API returns.

Hey Chris, you are right. I was accidentally using your categorization endpoint instead of tags endpoint. The tag results seem to be good.

By the way, I'm on the look-out for other image classification services or open-source projects. I know I can (and will) deep dive into Tensor Flow deep-learning, but any other suggestions are really welcome!

If you're looking to train your neural net for a specific narrow category, then you cannot beat something like tensorflow and training it yourself (and it's very easy: https://www.tensorflow.org/versions/master/how_tos/image_ret...). If you want a general image classifier (most people dont) then you'd probably want to use a service.

I regularly benchmark all image captioning approach, but human captioning is still WAY better (see http://www.cloudsightapi.com/api, scroll down for demo)

and probably way more expensive? But yeah, i have never seen a more accurate captioning for images yet

I think you might be interested in SemanticMD. http://www.semantic.md/

We provide a medical image analysis platform. I work at SemanticMD.

https://www.imageidentify.com/ (by Wolfram)

I recently used indico.io for a project, and I'm a huge fan of what they offer.

From my brief interactions, they seem like awesome people too.

At least when I test that image now it seems to only tag person.

Yes, that's what the website demo returns back. The full API response is what I reported.

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