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

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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

# Overview
Nowadays, developers use PyTorch or Tensorflow frameworks to train image classification models. This course focuses on PyTorch implementation via an open-source Python library called the **PyTorch Image Model**.

The **PyTorch Image Model (timm)** is a deep-learning framework created by Ross Wightman. It contains models, utilities, data loaders, and scripts related to computer vision. Most of the base implementation relies on the state-of-the-art algorithms adapted for PyTorch.

The main objective of this framework is to provide an out-of-the-box library for researchers and developers to reproduce training results for ImageNet.

It’s in active development and is suitable for testing various state-of-the-art architectures for image classification.

The PyTorch Image Model framework offers a lot of possible architectures with the option to change it with a single line of code. Some of the most popular architectures include:

* MixNet-XL
* SE-ResNeXt-26-D
* EfficientNet-B0
* EfficientNet-B2
* EfficientNet-B3
* EfficientNet-ES
* ResNet50
* MobileNetV3-Large-100

In this course, we’ll focus on the usage of the following architectures:
* ResNet50
* EfficientNet-B0

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# Validation
The PyTorch Image Model also provides its own [validation results](https://rwightman.github.io/pytorch-image-models/results/). Developers can cross-check the output and benchmark the performance of their desired models.

Based on its latest benchmark, ResNet50 achieved 79.038 in top-1 accuracy. The top-5 accuracy was 94.390.


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# License

## Code
The code provided by this framework is based on the [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0.html) license. We can train custom classification models with it for commercial purposes.

## Pre-trained weights (ImageNet)
Most of the pre-trained weights provided by this framework were trained using [ImageNet](https://image-net.org/download) datasets, ImageNet was released for noncommercial purposes. If we intend to use the pre-trained weights directly, we have to seek legal advice on it.



## Pre-trained weights (Others)
Pre-trained weights such as Facebook’s WSL, SSL, SWSL ResNe(Xt), and the Google Noisy Student EfficientNet were trained using proprietary datasets with an explicit noncommercial license (CC-BY-NC 4.0).

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# Documentation
We can find the official documentation at [Pytorch Image Model](https://rwightman.github.io/pytorch-image-models/). There’s also alternative comprehensive documentation called [timmdocs](https://fastai.github.io/timmdocs/).



Learn the fundamental concepts of the PyTorch Image Model framework.

Introduction

Basic Concepts

Augmentation

Loss

Training

Model Conversion

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

Deployment

Appendix

Conclusion

PyTorch Image Model Framework

Overview