Project - Classify clothes using TensorFlow

About the Dataset

Fashion-MNIST is a dataset of Zalando's article 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. Zalando intends Fashion-MNIST to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms. It shares the same image size and structure of training and testing splits. Zalando seeks to replace the original MNIST dataset

• Training set - 60,000 examples
• Test set - 10,000 examples
• Each example is a 28x28 grayscale image
• 10 classes

How to load the dataset

You can load the dataset as -
import tensorflow as tf
fashion_mnist = tf.keras.datasets.fashion_mnist.load_data()

Content of Dataset

Each image is 28 pixels in height and 28 pixels in width, for a total of 784 pixels in total. - Each row is a separate image - Column 1 is the class label. - Remaining columns are pixel numbers (784 total) - Each value is the darkness of the pixel (1 to 255)

Labels

Each training and test example is assigned to one of the following labels:

• 0 - T-shirt/top

• 1 - Trouser

• 2 - Pullover

• 3 - Dress

• 4 - Coat

• 5 - Sandal

• 6 - Shirt

• 7 - Sneaker

• 8 - Bag

• 9 - Ankle boot

Train a CNN model on this dataset. You are expected to achieve an accuracy of more than 90% in this project

After you are done, save your notebook as classify_clothes.ipynb in your home folder on CloudxLab.