Improvements due to fine-tuning a ViT-B model when training on 1, 2, 5 or 10 images of the COVID IF dataset on the CPU (same CPU as in a ). We compare using the default SAM and our LM generalist model as starting points and evaluate the segmentation results on 36 test images (not part of any of the training sets). We use early stopping. Dotted lines indicate results obtained with LoRA 40 using a rank of 4. Otherwise all model parameters are updated, as in previous experiments; we refer to this as full fine-tuning (FFT) in the caption. See Extended Data Fig. 10d for training times of different hardware setups. Note that we use the segmentation accuracy evaluated at an intersection over union (IOU) threshold of 50%, as the metric here, because we found that mean segmentation accuracy was too stringent for the small objects to meaningfully compare improvements.
