A comprehensive PyTorch tutorial on implementing transfer learning for image classification, specifically demonstrating how to adapt pretrained models for classifying ants and bees. The tutorial showc
ases both fine-tuning and feature extraction approaches, achieving high accuracy despite limited training data.
Reasons to Read -- Learn:
how to implement transfer learning with PyTorch, a powerful technique that allows you to achieve high accuracy (>94%) even with small datasets of just 120 images per class
practical implementation details like data augmentation, learning rate scheduling, and model checkpointing, with complete working code examples using PyTorch's modern features
how to visualize and validate your model's predictions, including techniques for displaying image transformations and creating confusion matrices for model evaluation
publisher:
PyTorch
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