Send us an image, and we'll understand it using our model trained on 10,000 categories.
Recognize tens of thousands of categories, objects, and tags in any image.
Find similar images in large uncategorized repositories, using a smart combination of semantic and visual similarity. Great for detecting near-duplicates and visual search.
Clarifai is pushing the limits of practical artificial intelligence. Our image recognition systems held the top 5 spots for classifying objects in images in the ImageNet 2013 competition. Optimized systems go even further, achieving lower error rates at 10x the speed with 1/5th of the memory footprint.
Matthew studied machine learning and image recognition with the pioneers of deep learning and convolutional neural networks. His research produced the top 5 results in the 2013 ImageNet classification competition.
Adam was a software engineer at Google for 10+ years where he built the music recommender for Google Play. Most recently he worked on Goggles and visual search.
Michal graduated with a BS in Computer Science from Columbia University in May 2014, where he focused on Machine Learning and Computer Vision. As part of a combined plan program he also completed an Applied Mathematics BA from Queens College with honors in mathematics.
Vinay previously founded a Fashion-Tech startup, BrandBacker and was an engineer on the Adobe Flash Player. His expertise is in web crawling and building scalable back-end systems.