Transfer Learning

Transfer learning is the practice of taking a model trained on one task or dataset and reusing its learned representations for a different but related task. Instead of training from random weights on your specific dataset, you start with a model that already understands general visual features (edges, textures, shapes, object parts) from pre-training on a large dataset like ImageNet, COCO, or Objects365. This gives you a strong starting point that typically leads to faster convergence and better accuracy, especially when your target dataset is small.

The standard workflow has two steps: pre-training (training a backbone on a large general dataset, or using an off-the-shelf pre-trained model) and fine-tuning (continuing training on your specific dataset with a lower learning rate). You can fine-tune the entire model, freeze the backbone and only train the head, or progressively unfreeze layers from top to bottom. The right strategy depends on how much target data you have and how different your domain is from the pre-training data.

Transfer learning is why computer vision is practical for most teams today. Without it, training an accurate detector or segmenter would require millions of labeled images and weeks of GPU time. With a pre-trained backbone, you can achieve strong results on custom tasks with a few hundred to a few thousand labeled examples and a few hours of training. Nearly every production computer vision model uses transfer learning in some form.