Dataset Format
Explore the three main Dataset classes and their use cases.
Introduction to the dataset
The PyTorch Image Model framework comes with the following Dataset
classes:
ImageDataset
IterableImageDataset
AugMixDataset
Our training data needs to be in the following structure:
<base_folder>
├── train
│ ├── class1
│ ├── class2
│ ├── class3
│ ├── ...
│ └── classN
└── val
├── class1
├── class2
├── class3
├── ...
└── classN
Each subfolder represents the corresponding class and contains relevant images.
The ImageDataset
class
We can use the ImageDataset
classi to create the training, validation, and test datasets for our image classification model.
It accepts the following arguments:
class ImageDataset(root, parser, class_map, load_bytes, transform) -> Tuple[Any, Any]:
root
(str
): This is the path of our datasets.parser
(Union[ParserImageInTar, ParserImageFolder, str]
): This is the parser for our datasets. It accepts either an image in a folder or a tar file.class_map
(Dict[str, str]
): This is a dictionary containing the class mapping.load_bytes
(bool