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 object detection, 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.

基于大规模Transformer模型的SAM模型制约了其在广泛现实应用中的计算成本。为解决这个问题，我们提出了轻量级SAM模型EfficientSAMs，借助图像预训练方法SAMI，通过SAM图像编码器重建特征进行视觉表征学习，并在SA-1B上微调模型，实现对图像分类、对象检测、实例分割和语义对象检测等多个视觉任务的评估。结果显示，SAMI方法在面向零样本实例分割等任务上，我们的EfficientSAMs表现优于其他快速SAM模型，达到显著增益（例如，在COCO/LVIS上的AP提高了约4个点）。

高效SAM：基于掩码图像预训练的高效物体分割