In this project, I have bult a pipeline to process real-world, user supplied images. A pipeline that can be used within a web or mobile app. Given an image of a dog, our algorithm will idenity an estimate of the canine's breed. If supplied an image of a human, the code will identify the resembling dog breed. This project was a part of udacity's deep learning Nanodegree.
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The datasets that we are using is of dogs and humans to distinguish between them.
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We used OpenCV's implementation of Haar feature-based cascade classifiers to detect human faces in images. OpenCV provides many pre-trained face detectors, stored as XML files on github. We have downloaded one of these detectors and stored it in the haarcascades directory.
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We used a pre-trained model to detect dogs in images. Specifically VGG-16 model, along with weights that have been trained on ImageNet, a very large, very popular dataset used for image classification and other vision tasks. ImageNet contains over 10 million URLs, each linking to an image containing an object from one of 1000 categories.