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

# Supported variants
The PyTorch Image Model supports the following EfficientNet variants:

|Base model|Resolution|
|-|-|
|EfficientNetB0|224|
|EfficientNetB1|240|
|EfficientNetB2|260|
|EfficientNetB3|300|
|EfficientNetB4|380|

EfficientNetB0 is a good start for most use cases. Other variants require higher GPU memory since the base image resolution is higher.

# Training a new EfficientNet model
We can utilize the same training script to train a new EfficientNet image classification model. Run the following command in the terminal:

```
python train.py /app/dataset2 --model efficientnet_b0 --num-classes 4
```

# Other variants
To train a different variant of EfficientNet, simply change the input for the `model` argument. For example, the following command will train a new EfficientNetB1 model:

```
python train.py /app/dataset2 --model efficientnet_b1 --num-classes 4
```

# Custom epochs
By default, the number of epochs to train is 300. We can configure it by using the `epochs` argument. For example, the following command sets the training epochs to 500:

```
python train.py /app/dataset2 --model efficientnet_b0 --num-classes 4 --epochs 500
```

# The `AutoAugment` augmentation
We can enable auto-augmentation by setting the `aa` flag as follows:

```
python train.py /app/dataset2 --model efficientnet_b0 --num-classes 4 --aa 'v0'
```

# The `Mixup` or `Cutmix` augmentations
To enable `Mixup` augmentations, set the `mixup` argument as follows:

```
python train.py /app/dataset2 --model efficientnet_b0 --num-classes 4 --mixup 0.5
```

Set the `cutmix` argument for `Cutmix` augmentation:

```
python train.py /app/dataset2 --model efficientnet_b0 --num-classes 4 --cutmix 0.5
```

We can enable both augmentations with the following command:

```
python train.py /app/dataset2 --model efficientnet_b0 --num-classes 4 --mixup 0.5 --cutmix 0.5 --mixup-switch-prob 0.3
```

> **Note**: Configure the `mixup-switch-prob` to switch between both augmentations randomly.

# The `Augmix` augmentation
We can use the following command for `Augmix` augmentation:

```
python train.py /app/dataset2 --model efficientnet_b0 --num-classes 4 --aug-splits 3 --jsd
```

Most state-of-the-art models use augmentations as part of the training process. Augmentations help to improve the performance of our model.


Learn to train an EfficientNet image classification model.

Introduction

Basic Concepts

Augmentation

Loss

Training

Model Conversion

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

Deployment

Appendix

Conclusion

EfficientNet

Supported variants

Base model	Resolution
EfficientNetB0	224
EfficientNetB1	240
EfficientNetB2	260
EfficientNetB3	300
EfficientNetB4	380