PyTorch Transfer Learning Project: Fine-Tune vs Feature Extraction ResNet18

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"Just use transfer learning" sounds simple—until you have to choose between freezing, partial fine-tuning, or full fine-tuning and defend that decision with real results. In this end-to-end tutorial, you’ll learn transfer learning the way it’s actually used in industry: with clear context, fair comparisons, and practical trade-offs.

We’ll simulate a real data science/machine learning job scenario: an e-commerce clothing company that needs an image classifier to auto-tag product photos (T-shirt, coat, sneaker, bag, etc.) so recommendations and search results don’t break. You’ll see how to take a pretrained ResNet18 (ImageNet) and adapt it to Fashion-MNIST, including reproducible experiment setup, train/validation/test splits, ImageNet-compatible preprocessing (224×224, grayscale → 3-channel, normalization), realistic augmentations, and a clean training + evaluation harness. Then we’ll compare a CNN from scratch vs. transfer learning strategies—feature extraction (freeze backbone), gradual unfreezing with discriminative learning rates, and full fine-tuning—plus quick ablations like weight decay and label smoothing to understand generalization.

Chapters:
00:00 - What we do in this video
00:30 - Who I am
01:09 - Problem description
02:40 - An overview of what we do in the course
05:02 - Data science bootcamp
05:45 - Google Colab notebook walkthrough

Видео PyTorch Transfer Learning Project: Fine-Tune vs Feature Extraction ResNet18 канала Ardavan Borzou