4.3
Intermediate
4h
Using PyTorch for Image Classification and Object Detection
Gain insights into using PyTorch for image classification and detection, delve into model implementation, and explore deployment on edge devices with ONNX and OpenVINO.
Image classification and object detection have gained widespread use in recent years. Content categorization and monitoring, disease diagnosis from medical images, identifying terrain in satellite images, and detecting road elements for self-driving cars are classification problems at their core. PyTorch is a popular framework for these tasks—offering a useful mix of user-friendliness, deep learning functionalities, customization, and optimization.
In this course, you will cover the fundamentals of classification and object detection models and apply them to actual datasets using PyTorch. You’ll learn popular architectures and how to implement and fine-tune them for better results. Finally, you’ll learn to convert models to ONNX and OpenVINO to deploy in edge devices.
By the end of this course, you will have acquired the necessary skills to be able to use PyTorch for image identification and object detection in real-world applications
Image classification and object detection have gained widespread use in recent years. Content categorization and monitoring, dis...Show More
WHAT YOU'LL LEARN
An understanding of core concepts related to image classification, object detection, and convolutional neural networks
Hands-on experience using image classification architectures
Familiarity with PyTorch for image classification tasks
Working knowledge of ONNX and OpenVINO for deploying deep learning models
An understanding of core concepts related to image classification, object detection, and convolutional neural networks
Show more
Content
1.
Before We Start
3 Lessons
Get familiar with image classification, object detection, and neural network basics with PyTorch.
2.
Basics of Convolutional Neural Networks
5 Lessons
Grasp the fundamentals of CNNs, including convolution operations, pooling, and batch normalization.
3.
Popular Neural Network Architectures for Image Classification
9 Lessons
Examine various neural network architectures for image classification, highlighting key advancements and efficiencies.
4.
Using PyTorch for Image Classification
6 Lessons
Grasp the fundamentals of using PyTorch for efficient image classification and model fine-tuning.
5.
Model Deployment
2 Lessons
Solve problems in deploying and optimizing AI models across various hardware frameworks and platforms.
6.
Basics of Object Detection
3 Lessons
Tackle object detection's complexities and distinguish between one-stage and two-stage architectures.
7.
Two-Stage Object Detection Architectures
5 Lessons
Build on two-stage object detection advancements with R-CNN, Fast R-CNN, and Faster R-CNN.
8.
One-Stage Object Detection Architectures
7 Lessons
Step through one-stage object detection architectures focusing on YOLO and SSD models.
9.
YOLOv7 Model Train and Inference on Edge
4 Lessons
Get started with training, deploying, and optimizing the YOLOv7 model for edge devices.
10.
Conclusion
1 Lessons
Break apart key strategies for using PyTorch in image classification and object detection.
11.
Appendix
1 Lessons
Break down complex ideas for managing Python environments with Anaconda.
Certificate of Completion
Showcase your accomplishment by sharing your certificate of completion.
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