The pre-trained models to consider are VGG16, VGG19, Inceptionv3, Xception, ResNet50, InceptionResNetv2, and MobileNet. What if instead of creating and training deep neural networks from scratch, I would take the pre-trained weights of these deep neural network architectures (trained with ImageNet datasets) and use them for my own dataset? ..
The traditional machine learning approach uses image feature extraction with local binary patterns, histogram-oriented gradients, global descriptors such as color histograms, or local descriptors such as SIFT, SURF, and ORB. These are handmade features that require a domain. Level of expertise.
But here comes the deep neural network! Deep Neural Nets automatically learns these features hierarchically from images, rather than using handmade features. The lower layer learns low-level features such as corners and edges, the middle layer learns colors and shapes, and the upper layer learns high-level features that represent objects in the image.
Therefore, the convolutional neural network can be used as a feature extractor by getting the activation available before the last fully connected layer of the network (ie, before the last softmax classifier). .. These activations act as a feature vector for the machine learning classifier, which continues to learn to classify it. This type of approach is suitable for image classification problems where the CNN is not trained from the beginning (it takes time and effort) but is a pre-trained CNNco. The Keras documentation and GitHub repository are well maintained and should be easy to understand.
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