![]() Let's get PyTorch loaded and then follow best practice to setup our code to be device-agnostic. Specifically, we're going to cover: Topic We'll use torchvision.datasets as well as our own custom Dataset class to load in images of food and then we'll build a PyTorch computer vision model to hopefully be able to classify them. The goal will be to load these images and then build a model to train and predict on them. We're going to be applying the PyTorch Workflow we covered in notebook 01 and notebook 02 to a computer vision problem.īut instead of using an in-built PyTorch dataset, we're going to be using our own dataset of pizza, steak and sushi images. In that case, we can always subclass and customize it to our liking. PyTorch includes many existing functions to load in various custom datasets in the TorchVision, TorchText, TorchAudio and TorchRec domain libraries.īut sometimes these existing functions may not be enough. Or if we were trying to build a recommendation system for customers purchasing things on our website, our custom dataset might be examples of products other people have bought. Or if we were trying to build a sound classification app, our custom dataset might be sound samples alongside their sample labels. Or if we were trying to build a model to classify whether or not a text-based review on a website was positive or negative, our custom dataset might be examples of existing customer reviews and their ratings. In essence, a custom dataset can be comprised of almost anything.įor example, if we were building a food image classification app like Nutrify, our custom dataset might be images of food. Model 1: TinyVGG with Data Augmentationĩ.1 Create transform with data augmentationĩ.2 Create train and test Dataset's and DataLoader'sġ1.1 Loading in a custom image with PyTorchġ1.2 Predicting on custom images with a trained PyTorch modelġ1.3 Putting custom image prediction together: building a functionĪ custom dataset is a collection of data relating to a specific problem you're working on. What should an ideal loss curve look like?Ĩ.3 The balance between overfitting and underfittingĩ. Model 0: TinyVGG without data augmentationħ.1 Creating transforms and loading data for Model 0ħ.3 Try a forward pass on a single image (to test the model)ħ.4 Use torchinfo to get an idea of the shapes going through our modelħ.6 Creating a train() function to combine train_step() and test_step()Ĩ. Other forms of transforms (data augmentation)ħ. Option 2: Loading Image Data with a Custom Datasetĥ.1 Creating a helper function to get class namesĥ.2 Create a custom Dataset to replicate ImageFolderĥ.3 Create a function to display random imagesĥ.4 Turn custom loaded images into DataLoader'sĦ. Option 1: Loading Image Data Using ImageFolderĥ. Become one with the data (data preparation)ģ.1 Transforming data with ansformsĤ. Importing PyTorch and setting up device-agnostic codeĢ. ![]()
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