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--- |
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license: apache-2.0 |
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pipeline_tag: mask-generation |
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--- |
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# EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss |
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- [Paper](https://arxiv.org/abs/2402.05008) |
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- [GitHub](https://github.com/mit-han-lab/efficientvit) |
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- [Demo](https://evitsam.hanlab.ai/) |
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## Pretrained Models |
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Latency/Throughput is measured on NVIDIA Jetson AGX Orin, and NVIDIA A100 GPU with TensorRT, fp16. Data transfer time is included. |
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| Model | Resolution | COCO mAP | LVIS mAP | Params | MACs | Jetson Orin Latency (bs1) | A100 Throughput (bs16) | Checkpoint | |
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|----------------------|:----------:|:----------:|:---------:|:------------:|:---------:|:---------:|:------------:|:------------:| |
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| EfficientViT-SAM-L0 | 512x512 | 45.7 | 41.8 | 34.8M | 35G | 8.2ms | 762 images/s | [link](https://huggingface.co/han-cai/efficientvit-sam/resolve/main/l0.pt) | |
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| EfficientViT-SAM-L1 | 512x512 | 46.2 | 42.1 | 47.7M | 49G | 10.2ms | 638 images/s | [link](https://huggingface.co/han-cai/efficientvit-sam/resolve/main/l1.pt) | |
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| EfficientViT-SAM-L2 | 512x512 | 46.6 | 42.7 | 61.3M | 69G | 12.9ms | 538 images/s | [link](https://huggingface.co/han-cai/efficientvit-sam/resolve/main/l2.pt) | |
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| EfficientViT-SAM-XL0 | 1024x1024 | 47.5 | 43.9 | 117.0M | 185G | 22.5ms | 278 images/s | [link](https://huggingface.co/han-cai/efficientvit-sam/resolve/main/xl0.pt) | |
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| EfficientViT-SAM-XL1 | 1024x1024 | 47.8 | 44.4 | 203.3M | 322G | 37.2ms | 182 images/s | [link](https://huggingface.co/han-cai/efficientvit-sam/resolve/main/xl1.pt) | |
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<p align="center"> |
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<b> Table1: Summary of All EfficientViT-SAM Variants.</b> 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. |
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</p> |
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## Usage |
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```python |
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# segment anything |
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from efficientvit.sam_model_zoo import create_sam_model |
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efficientvit_sam = create_sam_model( |
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name="xl1", weight_url="assets/checkpoints/sam/xl1.pt", |
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) |
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efficientvit_sam = efficientvit_sam.cuda().eval() |
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``` |
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```python |
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from efficientvit.models.efficientvit.sam import EfficientViTSamPredictor |
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efficientvit_sam_predictor = EfficientViTSamPredictor(efficientvit_sam) |
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``` |
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```python |
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from efficientvit.models.efficientvit.sam import EfficientViTSamAutomaticMaskGenerator |
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efficientvit_mask_generator = EfficientViTSamAutomaticMaskGenerator(efficientvit_sam) |
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``` |
