Fine-tuning takes a model that was pre-trained on a large dataset (like ImageNet, COCO, or Objects365) and continues training it on your specific, usually smaller, dataset. Instead of learning visual features from scratch, the model starts with general knowledge about edges, textures, shapes, and objects, then adapts to your particular classes, image style, and domain. This transfers knowledge from the large dataset to your task, dramatically reducing the amount of labeled data and training time needed.

The standard approach freezes early layers (which capture generic low-level features) and trains later layers plus a new classification/detection head on your data. Full fine-tuning updates all weights with a small learning rate, which works better when your dataset is large enough to avoid overfitting. Learning rate warmup (starting very small and increasing) prevents the pre-trained weights from being destroyed in the first few steps. Discriminative learning rates (lower for early layers, higher for later layers) are another common technique.

Fine-tuning is how most real-world computer vision models are built. Pre-trained YOLO models are fine-tuned for custom object detection, pre-trained segmentation models are adapted for medical or industrial use, and pre-trained VLMs are fine-tuned for domain-specific visual question answering. Datature Nexus provides built-in fine-tuning workflows with automatic hyperparameter selection.