Tire defect detection has significant industrial value and has been a research topic in both academia and industry. Despite its importance, prior works does not considered the practical manufacturing circumstances, where there are only limited annotation for the defect. Such limitation hinders the prior works from deploying to the real-world system. To address the problem of Tire Defect Detection with Limited Annotation (TTDLA), we proposed a novel framework, denoted as tire defect detection with Self-Supervision and Synthetic data (or S3). S3 first uses self-supervised learning to train the encoder without using any labeled data in the pretraining stage. The encoder is then adopted as the encoder of the Faster-RCNN detector in the fine-tuning stage. In addition, we proposed an algorithm to generate synthesized image by pasting defects randomly onto the regular image. Experiments demonstrate that both self-supervised learning and synthesized data boost the performance of the detector under TTDLA scenario.