I was studying a course on udacity they gave a task to classify different fashion images and also train the model to get the maximum accuracy.I was able to achive accuracy up to 97% to 98% with this model.
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.You can read more about this at Kaggle
Fashion MNIST Classification uses a number of open source projects to work properly:
- numpy - NumPy is the fundamental package for scientific computing with Python
- pytorch - An open source deep learning platform that provides a seamless path from research prototyping to production deployment
- matplotlib - Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms
- torchvision - The torchvision package consists of popular datasets, model architectures, and common image transformations for computer vision
- python - Python is an interpreted, high-level, general-purpose programming language
- python 3.6.+
- Tensorflow
- Pytorch
- Numpy
- Matplotlib
Your task is to improve this model and achive a better result then i did.