![]() ![]() It has over 60,000 training images and 10,000 test images. MNIST: MNIST is a dataset consisting of handwritten images that are normalized and center-cropped. Here’s a quick overview of datasets that are included in the classes torchvision and torchtext. This package consists of datasets that are related to text. There is one more package named torchtext which has all the basic utilities of PyTorch Natural Language Processing. What’s in the package torch and torchvision? The package torch consists of all the core classes and methods required to implement neural networks, while torchvision is a supporting package consisting of popular datasets, model architectures, and common image transformations for computer vision. Does that ring any bells? In the previous example, when we were classifying MNIST images, we used the same class to download our images. PyTorch comes with several built-in datasets, all of which are pre-loaded in the class torch.datasets. Before that, we’ll have a quick look at the datasets that are included in the PyTorch library. We’ll learn about working on custom datasets in the next sections. But when you are working on a dataset of your own, it’s quite tricky and challenging to achieve high accuracy. The reason being, these datasets are neatly organized and easy to preprocess. While working on these, we can easily achieve accuracy greater than 90% for prediction- and classification-type problems. ![]() ![]() In many cases, we train neural networks on default or well-known datasets like MNIST or CIFAR. Hence it is important to understand, preprocess, and load your data into the network in a more intuitive way. This is because data is like fuel for your network: the more appropriate it is, the faster and the more accurate the results are! One of the main reasons for your neural network to underperform might be due to bad, or poorly understood data. If you are working on a real-time project involving Deep Learning, it's common that most of your time goes into handling data, rather than the neural network that you would build. ![]()
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