Progressive Resizing is a training technique that starts training with small, low-resolution images and progressively increases the resolution — inspired by progressive growing in GANs, this approach yields faster training and often better generalization by building feature hierarchies from coarse to fine.

How Progressive Resizing Works

• Start Small: Begin training with small images (e.g., 64×64) — fast iterations, rapid feature learning.
• Increase: Periodically double the resolution (64→128→224→448) — model refines features at each scale.
• Learning Rate: Optionally reset or warm up the learning rate at each resolution increase.
• Transfer: Lower-resolution features transfer to higher resolution — warm-starting accelerates training.

Why It Matters

• Speed: Low-resolution training is 4-16× faster — majority of training epochs run at low resolution.
• Regularization: Starting at low resolution acts as a regularizer — model learns to extract the most important features first.
• fast.ai: Popularized by fast.ai as a key technique for efficient, high-quality training.

Progressive Resizing is training from blurry to sharp — starting with fast low-resolution training and progressively refining to full resolution.