CNN-on-FashionMNIST-Using-Keras

Implemented a CNN model using keras and tensorflow on fashion mnist. Achieved more than 93% accuracy.

We will implement a Convolutional Neural Network in Keras

Then we will apply our CNN to Fashion MNIST dataset

Download the Fashion-MNIST dataset

The MIST data set of handwritten digits

• is too easy (convolutional nets can achieve 99.7%)
• is overused.
• can not represent modern Computer Vision tasks

Fashion-MNIST consists of 60,000 training images and 10,000 test images. It is a MNIST-like fashion product database. The developers believe MNIST has been overused so they created this as a direct replacement for that dataset. Each image is in greyscale and associated with a label from 10 classes.

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.a

Fashion-MNIST serves 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.

• Size: 30 MB

• Number of Records: 70,000 images in 10 classes

• Images are 28x28 NumPy arrays, with pixel values ranging between 0 and 255.

• Labels are an array of integers, ranging from 0 to 9.

Label Description 0. T-shirt/top

1. Trouser
2. Pullover
3. Dress
4. Coat
5. Sandal
6. Shirt
7. Sneaker
8. Bag
9. Ankle boot

On https://github.com/zalandoresearch/fashion-mnist there is a list of benchmarks and the related NN architectures.