PyTorch: The Ultimate Course from Beginner to Advanced - Part7

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🎥 Want to master transfer learning in PyTorch and build an image classifier that performs like a pro, even with limited data? In this seventh part of our PyTorch Tutorial Course, we’ll train a powerful food image classifier for pizza, steak, and sushi using EfficientNet-B0 — from feature extraction to fine-tuning, step by step.

We’ll download a ready-to-use dataset, prepare DataLoaders with the exact transforms expected by pretrained models, and build a clean, GPU-ready pipeline. You’ll first train the model as a feature extractor (freezing the backbone for speed), then fine-tune the top layers for that extra boost in accuracy. Finally, we’ll evaluate with confusion matrices, inspect the most wrong predictions, and even test the model on your own images to see it in action.

💻 Code on GitHub: https://github.com/frezazadeh/Pytorch...
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🔖 Chapters & Timestamps

00:00:00 1. Introduction – What is Transfer Learning?
00:01:48 2. Setting up the Environment & Dataset
00:07:08 3. Auto-Transforms and DataLoaders for Pretrained Models
00:10:50 4. Building an EfficientNet-B0 Classifier
00:15:20 5. Phase 1: Feature Extraction Training
00:16:57 6. Phase 2: Fine-Tuning with Differential Learning Rates
00:18:46 7. Evaluation – Accuracy, Confusion Matrix & Misclassifications
00:23:05 8. Inference – Predicting Custom Images
00:24:35 9. Exercises & Next Steps

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📚 What You’ll Learn
• How to set up transfer learning with pretrained backbones (EfficientNet, ResNet, ConvNeXt, ViT)
• The difference between feature extraction and fine-tuning
• How to apply auto-transforms that match pretrained weights
• Training with differential learning rates for stability
• Evaluating results with confusion matrices and misclassified samples
• Running inference on custom images from URLs or uploads
• Practical tips for extending to your own datasets

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✅ Why Watch This Video?
• Clear, step-by-step explanation of transfer learning in practice
• Hands-on demonstration with EfficientNet-B0 on real images
• Learn best practices for freezing, unfreezing, and fine-tuning layers
• Build intuition for what makes pretrained models powerful

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👍 If this tutorial helped you, please:
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💬 Join the conversation:
Which pretrained model do you want to explore next — ResNet, ViT, or ConvNeXt?
How did your fine-tuning accuracy compare to the feature extraction phase?

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