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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.

Transfer Learning and Superconvergence

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!