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Getting Started with Image Classification with PyTorch
Gain insights into image classification with PyTorch. Learn about data preprocessing, model training, fine-tuning, and deploying models using ONNX for real-world applications.
4.6
50 Lessons
6h
Join 2.9 million developers at
Join 2.9 million developers at
LEARNING OBJECTIVES
- A basic overview of the PyTorch Image Model
- The ability to fine-tune custom image classification models
- A working knowledge of deploying models as REST API
- A familiarity with converting PyTorch models into ONNX format
Learning Roadmap
1.
Introduction
Introduction
Get familiar with image classification, techniques, metrics, and PyTorch Image Model framework.
2.
Basic Concepts
Basic Concepts
Look at essential PyTorch image classification, including models, datasets, preprocessing, and inference.
3.
Augmentation
Augmentation
7 Lessons
7 Lessons
Examine augmentation techniques to diversify datasets, improve model performance, and mitigate overfitting.
4.
Loss
Loss
4 Lessons
4 Lessons
Grasp the fundamentals of loss functions to improve model accuracy in PyTorch.
5.
Training
Training
7 Lessons
7 Lessons
Solve problems in image classification training using PyTorch, models, and techniques.
6.
Model Conversion
Model Conversion
9 Lessons
9 Lessons
Follow the process of converting and serving models across PyTorch, ONNX, TensorFlow, and TFLite.
7.
Deployment
Deployment
5 Lessons
5 Lessons
Practice using FastAPI to deploy image classification models with HTTP methods and REST API integration.
8.
Appendix
Appendix
5 Lessons
5 Lessons
Learn how to use virtual environments, Python packages, training arguments, and deployment dependencies.
Certificate of Completion
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Developed by MAANG Engineers
ABOUT THIS COURSE
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.
ABOUT THE AUTHOR
Ng Wai Foong
Content writer and AI specialist in speech synthesis, object detection and neural machine translation.
Trusted by 2.9 million developers working at companies
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Anthony Walker
@_webarchitect_
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Evan Dunbar
ML Engineer
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Software Developer
Carlos Matias La Borde
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Souvik Kundu
Front-end Developer
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Vinay Krishnaiah
Software Developer
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