This repository contains the code for an image classification project using TensorFlow and Keras on the Fashion MNIST dataset. Fashion MNIST is a popular dataset for beginners in the field of computer vision and deep learning, as it provides a simple and manageable task for image classification.
In this project, we utilize the powerful TensorFlow library along with Keras API to build and train a convolutional neural network (CNN) model to classify images of fashion items into various categories. The goal is to achieve high accuracy in predicting the type of clothing depicted in the images.
The Fashion MNIST dataset consists of 70,000 grayscale images belonging to 10 different classes. Each image is a 28x28 pixel square, representing a fashion item at a low resolution. The dataset is divided into 60,000 training images and 10,000 testing images.
The 10 classes in the dataset are:
| 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 |
Make sure you have the following libraries installed:
- TensorFlow
- NumPy
- Matplotlib
You can install them using the following command:
pip install requirement.txt
Include information about the achieved accuracy and performance of your model here.
In this project, we successfully built and trained a CNN model using TensorFlow Keras to classify fashion images from the Fashion MNIST dataset. The model achieved good accuracy in predicting the type of clothing depicted in the images. This project serves as a starting point for more advanced computer vision tasks and deep learning projects.