  We present the results of an initial study focused on developing a visual AI
solution able to recognize individual dogs in unconstrained (wild) images
occurring on social media.
  The work described here is part of joint project done with Pet2Net, a social
network focused on pets and their owners. In order to detect and recognize
individual dogs we combine transfer learning and object detection approaches on
Inception v3 and SSD Inception v2 architectures respectively and evaluate the
proposed pipeline using a new data set containing real data that the users
uploaded to Pet2Net platform. We show that it can achieve 94.59% accuracy in
identifying individual dogs. Our approach has been designed with simplicity in
mind and the goal of easy deployment on all the images uploaded to Pet2Net
platform.
  A purely visual approach to identifying dogs in images, will enhance Pet2Net
features aimed at finding lost dogs, as well as form the basis of future work
focused on identifying social relationships between dogs, which cannot be
inferred from other data collected by the platform.
