EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything

Yunyang Xiong, Bala Varadarajan, Lemeng Wu, Xiaoyu Xiang, Fanyi Xiao, Chenchen Zhu, Xiaoliang Dai, Dilin Wang, Fei Sun, Forrest Iandola, Raghuraman Krishnamoorthi, Vikas Chandra; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 16111-16121

Segment Anything Model (SAM) has emerged as a powerful tool for numerous vision applications. A key component that drives the impressive performance for zero-shot transfer and high versatility is a super large Transformer model trained on the extensive high-quality SA-1B dataset. While beneficial the huge computation cost of SAM model has limited its applications to wider real-world applications. To address this limitation we propose EfficientSAMs light-weight SAM models that exhibits decent performance with largely reduced complexity. Our idea is based on leveraging masked image pretraining SAMI which learns to reconstruct features from SAM image encoder for effective visual representation learning. Further we take SAMI-pretrained light-weight image encoders and mask decoder to build EfficientSAMs and finetune the models on SA-1B for segment anything task. We perform evaluations on multiple vision tasks including image classification object detection instance segmentation and semantic segmentation and find that our proposed pretraining method SAMI consistently outperforms other masked image pretraining methods. On segment anything task such as zero-shot instance segmentation our EfficientSAMs with SAMI-pretrained lightweight image encoders perform favorably with a significant gain (e.g. 4 AP on COCO/LVIS) over other fast SAM models. Our EfficientSAM code and models are available at https://github.com/yformer/EfficientSAM.