4.6
Beginner
6h
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.
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.
PyTorch is a machine learning framework used in a wide array of popular applications, including Tesla’s Autopilot and Pyro, Uber...Show More
WHAT YOU'LL LEARN
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
A basic overview of the PyTorch Image Model
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TAKEAWAY SKILLS
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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Course Author:
Developed by MAANG Engineers
Meet the Instructor
Ng Wai Foong
Software Engineer at Educative
Every Educative resource is designed by our team of ex-MAANG software engineers and PhD computer science educators — subject matter experts who've shipped production code at scale and taught the theory behind it. No videos, no fluff.
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