EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss

• Paper
• GitHub
• Demo

Pretrained Models

Latency/Throughput is measured on NVIDIA Jetson AGX Orin, and NVIDIA A100 GPU with TensorRT, fp16. Data transfer time is included.

Model	Resolution	COCO mAP	LVIS mAP	Params	MACs	Jetson Orin Latency (bs1)	A100 Throughput (bs16)	Checkpoint
EfficientViT-SAM-L0	512x512	45.7	41.8	34.8M	35G	8.2ms	762 images/s	link
EfficientViT-SAM-L1	512x512	46.2	42.1	47.7M	49G	10.2ms	638 images/s	link
EfficientViT-SAM-L2	512x512	46.6	42.7	61.3M	69G	12.9ms	538 images/s	link
EfficientViT-SAM-XL0	1024x1024	47.5	43.9	117.0M	185G	22.5ms	278 images/s	link
EfficientViT-SAM-XL1	1024x1024	47.8	44.4	203.3M	322G	37.2ms	182 images/s	link

Table1: Summary of All EfficientViT-SAM Variants. COCO mAP and LVIS mAP are measured using ViTDet's predicted bounding boxes as the prompt. End-to-end Jetson Orin latency and A100 throughput are measured with TensorRT and fp16.

Usage

# segment anything
from efficientvit.sam_model_zoo import create_sam_model

efficientvit_sam = create_sam_model(
  name="xl1", weight_url="assets/checkpoints/sam/xl1.pt",
)
efficientvit_sam = efficientvit_sam.cuda().eval()

from efficientvit.models.efficientvit.sam import EfficientViTSamPredictor

efficientvit_sam_predictor = EfficientViTSamPredictor(efficientvit_sam)

from efficientvit.models.efficientvit.sam import EfficientViTSamAutomaticMaskGenerator

efficientvit_mask_generator = EfficientViTSamAutomaticMaskGenerator(efficientvit_sam)