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▤ Summary

Deep Convolutional Neural Networks have become the state of the art methods for image classification tasks. This is a type of model network architecture that contains two or more identical subnetworks which are used to generate feature vectors(or embeddings) for each input and compare them. Siamese Networks can be applied in use cases, like face recognition, detecting duplicates, and finding anomalies. For this, we will provide three images to the model, where two of them will be similar (anchor and positive samples), and the third will be unrelated (a negative example). Now, during the training process, we require a large number of images for each of the classes (cats, dogs, horses, and elephants).

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