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

Which argument determines the number of checkpoints to keep?


Which argument determines the number of checkpoints to keep?

Which evaluation metric represents the training loss during each training step?


Which evaluation metric represents the training loss during each training step?

Which argument determines the number of training epochs?


Which argument determines the number of training epochs?

Which argument will resume a full model with the optimizer state?

Which argument will resume a full model with the optimizer state?

Which arguments are related to training with an exponential moving average (EMA)?

Which arguments are related to training with an exponential moving average (EMA)?

Training

Training

Test your knowledge of the training processes in the PyTorch Image Model.

Introduction

Basic Concepts

Augmentation

Loss

Training

Model Conversion

Deployment

Appendix

Conclusion

Quiz (Training)