Train/Fine-Tune Segment Anything 2 (SAM 2) in 60 Lines of Code

SAM2 (Segment Anything 2) by Meta is a versatile image segmentation model trained on an extensive dataset of 11 million images and 11 billion masks, making it highly effective for a wide range of segmentation tasks. While SAM2 can segment common objects well, it may underperform on rare or domain-specific tasks, necessitating fine-tuning to improve performance on specific datasets. This tutorial outlines how to fine-tune SAM2 for custom tasks in just 60 lines of code. The process involves downloading SAM2, preparing a dataset, and using a simple script to load images, segment masks, and select random points within those masks. The fine-tuning focuses on training the mask decoder and, optionally, the prompt encoder while freezing the image encoder. The tutorial also covers setting up the optimizer, employing mixed precision training, and running a training loop with custom loss functions to refine the model. The final step includes saving the fine-tuned model and using it for inference on new images, demonstrating the practical application of SAM2 in specialized segmentation tasks.