  The Segment Anything Model (SAM) is a promptable segmentation model recently
introduced by Meta AI that has demonstrated its prowess across various fields
beyond just image segmentation. SAM can accurately segment images across
diverse fields, and generating various masks. We discovered that this ability
of SAM can be leveraged to pretrain models for specific fields. Accordingly, we
have proposed a framework that utilizes SAM to generate pseudo labels for
pretraining thermal infrared image segmentation tasks. Our proposed framework
can effectively improve the accuracy of segmentation results of specific
categories beyond the SOTA ImageNet pretrained model. Our framework presents a
novel approach to collaborate with models trained with large data like SAM to
address problems in special fields. Also, we generated a large scale thermal
infrared segmentation dataset used for pretaining, which contains over 100,000
images with pixel-annotation labels. This approach offers an effective solution
for working with large models in special fields where label annotation is
challenging. Our code is available at https://github.com/chenjzBUAA/SATIR
