README
EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss
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)
Contributors
Similar repos
README
EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss
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)