Designed a Convolutional Neural Network to learn from a million wallpaper images. Test accuracy of over 90% was achieved after 36 epochs over the 17 unique classes. Class Activation Maps were used to check if the correct patterns were learned. A few patterns such as ‘CMM’ or ‘PG’ were learned perfectly.
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After training a CNN on a downsized VGGNet, gradcam had been used to visualze learned patterns from our own wallpaper dataset.
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navid0308/CNN-knowledge-evaluation
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After training a CNN on a downsized VGGNet, gradcam had been used to visualze learned patterns from our own wallpaper dataset.
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