Training VGG11 from Scratch using PyTorch If you do not have those, feel free to install them as you proceed. There a few other requirements like Matplotlib for saving graph plots and OpenCV for reading images. Follow the instructions according to your operating system and environment and choose the right version. You can visit the official PyTorch page to install the latest version of PyTorch. This will ensure that there are no conflicts with other versions and projects. Be sure to use an Anaconda or Python virtual environment to install the latest version. I insist that you install this version, or whatever the latest is when you are reading this. In this tutorial, we will use PyTorch version 1.8.0. You can download the source code and the test data for this tutorial by clicking on the button below.ĭownload Source Code and Test Data The PyTorch Version The image names and the digits they contain are the same so that we can easily differentiate between them. Finally input folder contains the test images that we will test our trained model on.The outputs folder will hold the loss and accuracy plots along with the trained VGG11 model.We will get into the details of these shortly. The src folder contains the three Python files that we will need in this tutorial.It will be easier for you to follow along if you use the same structure as well. We will follow the below directory structure for this project. This makes the work of procuring the dataset a bit easier. We surely cannot do that here as that requires a lot of computational power and training time as well.Īlso, we can load the MNIST dataset using the torchvision.dataset module. In the original paper, the authors trained the VGG models on the ImageNet dataset. We can surely look at bigger and more complex datasets in future posts. Our main goal is to learn how writing a model architecture on our own and training from scratch affects accuracy and loss. Why the Digit MNIST dataset? It is a simple dataset, it is small, and the model will very likely converge in a few epochs even when training from scratch. The Datasetįor training, we will use the Digit MNIST dataset. In this section, we will go over the dataset that we will use for training, the project directory structure, and the PyTorch version. The Directory Structure, Dataset, and PyTorch Version Let us get into the depth of the tutorial now and get into training VGG11 from scratch using PyTorch. Testing the trained model on digit images ( which are not part of the MNIST dataset).Analyzing the loss and accuracy plots after training.We will also see class-wise accuracy for each of the digit classes while validating with each epoch.We will also follow the same optimizer settings as mentioned in the original VGG paper.We will train our VGG11 from scratch using the Digit MNIST dataset.Going over the dataset and directory structure.So, what are we going to cover in this tutorial? Next week (part three): Implementing all the VGG models in a generalized manner using the PyTorch deep learning framework.This week (part two): Training our implemented VGG11 model from scratch using PyTorch.Last week (part one): Implementing VGG11 from scratch using PyTorch.This will give us a good idea of how building and training a model on our own from scratch feels like. This week, we will use the architecture from last week (VGG11) and train it from scratch. One of the results on our test data after training VGG11 from scratch using PyTorch.
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