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

TensorFlow is another deep learning framework for image classification. One of the main differences between PyTorch and TensorFlow is the default shape for an image tensor. PyTorch uses the NCHW format:

* `N`: This is the number of the batch size.
* `C`: This is the number of channels.
* `H`: This is the height of the image.
* `W`: This is the width of the image.

On the other hand, TensorFlow uses the NHWC format:
* `N`: This is the number of the batch size.
* `H`: This is the height of the image.
* `W`: This is the width of the image.
* `C`: This is the number of channels.



Learn to convert an ONNX model into a TensorFlow model.

Introduction

Basic Concepts

Augmentation

Loss

Training

Model Conversion

Deployment

Appendix

Conclusion

Convert ONNX into a Tensorflow Model