Image Tagging

Use Deep Learning to Identify Features in an Image

Select an image

Paste the URL to an image or UPLOAD

Autotag It

Or, try these sample images:

How it works

Quickly Identify Features and Automatically Tag Images

Our state-of-the-art deep learning models can dentify features in images, but different models produce slightly different results. We host a variety of different implementations, so you can pick the one(s) which work best for you!

The IllustrationTagger and InceptionNet microservices are just two of the many cloud-scaled CNN deep-learning models hosted on Algorithmia which can recognize hundreds of different features in images.

IllustrationTagger is a version of Saito, Masaki and Matsui, Yusuke. (2015). Illustration2Vec: A Semantic Vector Representation of Illustrations. SIGGRAPH Asia Technical Briefs. 2015. Learn more about their work here.

InceptionNet is a direct implementation of Google's InceptionNet, which was trained on the ImageNet 2015 dataset. It is implemented using Google's Tensorflow python bindings.

Let us know what you think @Algorithmia or by email.

Built For Developers

A simple, scalable API for machine intelligence


import Algorithmia

image = "_IMAGE_URL_"
client = Algorithmia.client("_API_KEY_")

tagger1 = client.algo("deeplearning/IllustrationTagger/0.2.5")
tagger2 = client.algo("deeplearning/InceptionNet/1.0.3")

print "IllustrationTagger:", tagger1.pipe({"image": image})
print "InceptionNet:", tagger2.pipe(image)


  "rating": [
    {"safe": 0.9961}
  "general": [
    {"no humans": 0.7062},
    {"monochrome": 0.6084}

  "tags": [
      "class": "cliff, drop, drop-off",
      "confidence": 0.4765
      "class": "alp",
      "confidence": 0.1755

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