Inspired by the Pinterest paper at KDD on implementing Visual Search, I have created this barebones but functional implementation of a Visual Search server using ~450,000 Female Fashion images crawled from an image aggregation website. The code uses Pool 3 layer features extracted from the Google&#x27;s latest Inception model using Tensor Flow. The extracted vectors are then indexed using an Approximate Nearest Neighbor implementation from Nearpy. The AMI provided contains both images and pre-computed vectors.<p>In future I plan to add more images ~2 Million in the same domain. Test various combination of nearest neighbor indexes and multiple vectors per images using some form of a multibox style detector. I will also add a script to launch spot GPU instances via Cloudformation to economically index images using S3 and SQS. I am building a companion iOS swift app however, since Tensor Flow hasn&#x27;t been ported to iOS yet, its still in development.<p><a href="https:&#x2F;&#x2F;engineering.pinterest.com&#x2F;blog&#x2F;building-scalable-machine-vision-pipeline" rel="nofollow">https:&#x2F;&#x2F;engineering.pinterest.com&#x2F;blog&#x2F;building-scalable-mac...</a>