What is the difference between the Fashion-MNIST dataset and the classic MNIST dataset?
The Fashion-MNIST dataset and the classic MNIST dataset are two popular datasets used in the field of machine learning for image classification tasks. While both datasets consist of grayscale images and are commonly used for benchmarking and evaluating machine learning algorithms, there are several key differences between them.
Firstly, the classic MNIST dataset contains images of handwritten digits (0-9) taken from the NIST Special Database 3 and NIST Special Database 1. The images in this dataset are 28×28 pixels in size and have a single channel (grayscale). The dataset consists of 60,000 training images and 10,000 test images, making a total of 70,000 images. Each image is labeled with the corresponding digit it represents, ranging from 0 to 9.
On the other hand, the Fashion-MNIST dataset is designed to be a drop-in replacement for the classic MNIST dataset, but with a focus on fashion products. It consists of 60,000 training images and 10,000 test images, like the classic MNIST dataset. However, instead of handwritten digits, the Fashion-MNIST dataset contains images of fashion products such as clothing, shoes, and accessories. These images are also 28×28 pixels in size and have a single channel (grayscale). Each image in the Fashion-MNIST dataset is labeled with one of the following categories: T-shirt/top, Trouser, Pullover, Dress, Coat, Sandal, Shirt, Sneaker, Bag, and Ankle boot.
The main motivation behind the creation of the Fashion-MNIST dataset was to provide a more challenging and realistic dataset for image classification tasks. While the classic MNIST dataset has been extensively studied and many machine learning algorithms achieve high accuracy on it, it is often criticized for being too easy and not representative of real-world scenarios. By introducing the Fashion-MNIST dataset, researchers and practitioners have a more diverse and complex dataset to work with, enabling them to develop and evaluate algorithms that are more robust and applicable to real-world problems.
In terms of application, the classic MNIST dataset is often used as a starting point for beginners in the field of machine learning. Its simplicity and small size make it ideal for learning the basics of image classification algorithms and evaluating their performance. On the other hand, the Fashion-MNIST dataset is suitable for more advanced tasks, where the goal is to classify fashion products accurately. This dataset can be used to train machine learning models for various applications, such as fashion recommendation systems, virtual try-on, and image-based search in e-commerce.
The Fashion-MNIST dataset and the classic MNIST dataset are two widely used datasets in the field of machine learning for image classification tasks. While the classic MNIST dataset consists of handwritten digits, the Fashion-MNIST dataset contains images of fashion products. The Fashion-MNIST dataset provides a more challenging and realistic dataset for evaluating machine learning algorithms in the context of fashion. Both datasets have their own applications and can be used to train and evaluate machine learning models for various image classification tasks.
Other recent questions and answers regarding EITC/AI/GCML Google Cloud Machine Learning:
- What types of algorithms for machine learning are there and how does one select them?
- When a kernel is forked with data and the original is private, can the forked one be public and if so is not a privacy breach?
- Can NLG model logic be used for purposes other than NLG, such as trading forecasting?
- What are some more detailed phases of machine learning?
- Is TensorBoard the most recommended tool for model visualization?
- When cleaning the data, how can one ensure the data is not biased?
- How is machine learning helping customers in purchasing services and products?
- Why is machine learning important?
- What are the different types of machine learning?
- Should separate data be used in subsequent steps of training a machine learning model?
View more questions and answers in EITC/AI/GCML Google Cloud Machine Learning