Gain insights into image classification with PyTorch. Learn about data preprocessing, model training, fine-tuning, and deploying models using ONNX for real-world applications.

Introduction to PyTorch image classification-01.png

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PyTorch is a machine learning framework used in a wide array of popular applications, including Tesla’s Autopilot and Pyro, Uber’s probabilistic modeling engine.

This course is an introduction to image classification using PyTorch’s computer vision models for training and tuning your own model. You’ll start with the fundamental concepts of applying machine learning and its applications to image classification before exploring the process of training your AI model. You’ll prepare data for intake by the computer vision model with image pre-processing, set up pipelines for training your model, and fine-tune the variables to improve predictive performance. You’ll finish by deploying the image classification model by converting to ONNX format and serving it via REST API.

By the end of this course, you’ll be able to build and deploy your own image classification models from scratch.

Getting Started with Image Classification with PyTorch

## 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`): This specifies whether to load as bytes.
* `transform` (`List`): This is a list of image transformations when loading our datasets.

### Parser
The `ImageDataset` contains a built-in `parser` object that’s built upon the `create_parser` factory method. The `parser` object will find all images defined by the `train` and `val` folders. The folder structure should be as follows:

```
train/class1/12345.png
train/class1/12346.png
train/class1/12347.png

...

train/class2/44122.png
train/class2/44123.png
train/class2/44124.png
```

The subfolders represent the label for the underlying images, such as if we train for a 5-class image classification model. We should have the following folder structure:

```
train/apple/...
train/banana/...
train/grape/...
train/orange/...
train/pear/...
```

The `parser` object provides the `class_to_idx` function to map the classes to integers. For example:

```
{'apple': 0, 'banana': 1, 'grape': 2, 'orange': 3, 'pear': 4}
```

There’s also an attribute called `samples` which returns a list of tuples:

```
[('train/apple/12345.png', 0), ('train/banana/22241.png', 1), ..., ('train/pear/44321.png', 4), ('train/pear/4479.png', 4), ...]
```

The `parser` object is subscribable, allowing us to get the items via index.

```
# syntax
parser[index]

# example
parser[0]
# returns ('train/apple/12345.png', 0)
``` 

### Example
Let’s look at another example using [Multi-class Weather Dataset for Image Classification](https://data.mendeley.com/datasets/4drtyfjtfy/1). The license for these datasets is Creative Commons Attribution 4.0 International. We can download the datasets by doing the following:




# 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`): This specifies whether to load as bytes.
* `transform` (`List`): This is a list of image transformations when loading our datasets.

## Parser
The `ImageDataset` contains a built-in `parser` object that’s built upon the `create_parser` factory method. The `parser` object will find all images defined by the `train` and `val` folders. The folder structure should be as follows:

```
train/class1/12345.png
train/class1/12346.png
train/class1/12347.png

...

train/class2/44122.png
train/class2/44123.png
train/class2/44124.png
```

The subfolders represent the label for the underlying images, such as if we train for a 5-class image classification model. We should have the following folder structure:

```
train/apple/...
train/banana/...
train/grape/...
train/orange/...
train/pear/...
```

The `parser` object provides the `class_to_idx` function to map the classes to integers. For example:

```
{'apple': 0, 'banana': 1, 'grape': 2, 'orange': 3, 'pear': 4}
```

There’s also an attribute called `samples` which returns a list of tuples:

```
[('train/apple/12345.png', 0), ('train/banana/22241.png', 1), ..., ('train/pear/44321.png', 4), ('train/pear/4479.png', 4), ...]
```

The `parser` object is subscribable, allowing us to get the items via index.

```
# syntax
parser[index]

# example
parser[0]
# returns ('train/apple/12345.png', 0)
``` 

## Example
Let’s look at another example using [Multi-class Weather Dataset for Image Classification](https://data.mendeley.com/datasets/4drtyfjtfy/1). The license for these datasets is Creative Commons Attribution 4.0 International. We can download the datasets by doing the following:




Explore the three main Dataset classes and their use cases.

Introduction

Basic Concepts

Augmentation

Loss

Training

Model Conversion

Anomaly Detection in Medical Images: Python, TF, and PyTorch

Deployment

Appendix

Conclusion

Dataset Format

Introduction to the dataset

The `ImageDataset` class

Anomaly Detection in Medical Images: Python, TF, and PyTorch

Dataset Format

Introduction to the dataset

The ImageDataset class

The `ImageDataset` class