Project 1 - Implement and deploy CNN model in real-time using python on Fashion MNIST dataset
Introduction
Humans are a brilliant natural system which is having the great capability of viewing and observing multiple images in a short span of time, then consume and deduce from them without even realizing that how the actual process is completed. But this case is not at all similar with machines or systems.
Aim and Objectives
The main aim to plan and strategize a solution by understanding the basic idea of image processing for better understanding of the underlying concepts and then to get a vision of how to feed an image in such a manner that the machine or system can consume it to help and give a fruitful output.
Any image is a cumulative arrangement of dots called pixels and the pixels are arranged in a special order to represent an image. If the order structure or color of a pixel is modified in whatsoever way, the representation of image would be altered as well. So elaborate work has to be done on the implementation of CNN model.
About the Dataset
Here the dataset provided is based on certain fashion components, it is a dataset of images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes.
Data Description
1. Each image is 28 pixels in height and 28 pixels in width, for a total of 784 pixels in total.
2. Each pixel has a single pixel-value associated with it, indicating the lightness or darkness of that pixel, with higher numbers meaning darker. This pixel-value is an integer between 0 and 255.
3. The training and test data sets have 785 columns.
4. The first column consists of the class labels (see above), and represents the article of clothing.
5. The rest of the columns contain the pixel-values of the associated image.
To locate a pixel on the image, suppose that we have decomposed x as x = i * 28 + j, where i and j are integers between 0 and 27. The pixel is located on row i and column j of a 28 x 28 matrix. For example, pixel31 indicates the pixel that is in the fourth column from the left, and the second row from the top, as in a ascii-diagram.