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PROJECT
Transfer Learning and Superconvergence
In this project, we’ll assist Sam, a photographer, in organizing 10,000 images of 10 rare bird species by building a bird classifier. We’ll utilize a pretrained ResNet model to apply transfer learning and superconvergence techniques like LR finders and OneCycle policy for swift convergence and exceptional results in the process of learning PyTorch.
You will learn to:
Perform transfer learning.
Modify the ResNet architecture.
Achieve superconvergence using the LR finder.
Work with schedulers in PyTorch.
Write training and inference loop from scratch in PyTorch.
Work with a custom dataset.
Skills
Deep Learning
Computer Vision
Prerequisites
Good understanding of Python
Good understanding of deep learning
Basic understanding of PyTorch and NumPy
Technologies
Python
Pillow
PyTorch
Matplotlib
Project Description
This project aims to develop a deep learning-based image classifier using a custom dataset on bird species. It emphasizes techniques like transfer learning and superconvergence for enhanced model performance.
We’ll follow a standard project flow, creating a dataset, designing a model architecture, establishing a training loop, and evaluating. We’ll also utilize the ResNet-18 architecture for transfer learning and leveraging pretrained models for faster training. The LR finder algorithm helps determine optimal learning rates, which is crucial for efficient convergence. We’ll also explore superconvergence principles, notably the OneCycle policy, for superior results.
We’ll utilize key libraries, including PyTorch, torchvision, Pillow and Matplotlib, and. Beyond model development, the project also fosters mastery in PyTorch, offering advanced techniques for quicker convergence and improved accuracy while deepening the understanding of model behavior.
Project Tasks
1
Build the Dataset
Task 0: System Overview
Task 1: Basic Data Analysis
Task 2: Prepare the Dataset
Task 3: Split into Train and Test Dataset
Task 4: Build DataLoaders
2
Build the Model
Task 5: Import Pretrained ResNet Architecture
Task 6: Freeze the Layer
Task 7: Modify the Architecture
Task 8: Test the Correctness
3
Write the Training and Inference Loop
Task 9: Define the Optimizer and Loss Function
Task 10: Write the Training Loop
Task 11: Write the Inference Loop
4
Train the Model
Task 12: Save and Load the Model
Task 13: Train the Model for the First Time
5
LR Finder and Scheduler
Task 14: Unfreeze 40% of the Model
Task 15: Linear Schedulers
Task 16: Cosine Scheduler
Task 17: Write the LR Finder Algorithm
Task 18: Model Training with Cosine Scheduler
6
Superconvergence (OneCycle Policy)
Task 19: OneCycle Policy
Task 20: Train the Model Using OneCycle Policy
Task 21: Compartive Study and Future Prospect
Congratulations!
Relevant Courses
Use the following content to review prerequisites or explore specific concepts in detail.