Clothes shopping is a taxing experience. My eyes get bombarded with too much information. Sales, coupons, colors, toddlers, flashing lights, and crowded aisles are just a few examples of all the signals forwarded to my visual cortex, whether or not I actively try to pay attention. The visual system absorbs an abundance of information. Should I go for that H&M khaki pants? Is that a Nike tank top? What color are those Adidas sneakers?
Can a computer automatically detect pictures of shirts, pants, dresses, and sneakers? It turns out that accurately classifying images of fashion items is surprisingly straight-forward to do, given quality training data to start from. In this tutorial, we’ll walk through building a machine learning model for recognizing images of fashion objects using the Fashion-MNIST dataset. We’ll walk through how to train a model, design the input and output for category classifications, and finally display the accuracy results for each model.
The fashion domain is a very popular playground for applications of machine learning and computer vision. The problems in this domain is challenging due to the high level of subjectivity and the semantic complexity of the features involved. I hope that this post has been helpful for you to learn about the 4 different approaches to build your own convolutional neural networks to classify fashion images. You can view all the source code in my GitHub repo at this link. Let me know if you have any questions or suggestions on improvement!
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