Deep Learning: How to build a dog detector and breed classifier using CNN?!
Briefly

You might think recognizing a dog’s breed in an image is an easy task for you.
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It might not be difficult to find dog breed pairs with minimal inter-class variation for instance, Curly-Coated Retrievers and American Water Spaniels.
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In the last decade, it is much easier to use deep learning techniques with a few lines of python code to distinguish between dog breeds in images.
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def face_detector(img_path):    img = cv2.imread(img_path)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray)
return len(faces) > 0
The performance of face_detector evaluated on 100 samples of human and dog images.
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Dog detector
It is time to use another detector that performs better on the dog dataset.I used pre-trained ResNet50 weights on ImageNet in Keras, which is trained on over 10 million images containing 1000 labels.
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The number of available images for the model to learn from is about 62 per kind, which might not be enough for CNN.
Read at Medium
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