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---
license: apache-2.0
pipeline_tag: mask-generation
---
# NanoSAM: Accelerated Segment Anything Model for Edge deployment
- [GitHub](https://github.com/binh234/nanosam)
- [Demo](https://huggingface.co/spaces/dragonSwing/nanosam)
## Pretrained Models
NanoSAM performance on edge devices. Latency/throughput is measured on NVIDIA Jetson Xavier NX, and NVIDIA T4 GPU with TensorRT, fp16. Data transfer time is included.
| Image Encoder | CPU | Jetson Xavier NX | T4 | Model size | Download |
| --------------- | :---: | :--------------: | :---: | :--------: | :------------------------------------------------------------------------------------------------------: |
| PPHGV2-B1 | 110ms | 9.6ms | 2.4ms | 12.7MB | [Link](https://huggingface.co/dragonSwing/nanosam/resolve/main/sam_hgv2_b1_ln_nonorm_image_encoder.onnx) |
| PPHGV2-B2 | 200ms | 12.4ms | 3.2ms | 29.5MB | [Link](https://huggingface.co/dragonSwing/nanosam/resolve/main/sam_hgv2_b1_ln_nonorm_image_encoder.onnx) |
| PPHGV2-B4 | 300ms | 17.3ms | 4.1ms | 61.4MB | [Link](https://huggingface.co/dragonSwing/nanosam/resolve/main/sam_hgv2_b1_ln_nonorm_image_encoder.onnx) |
| ResNet18 | 500ms | 22.4ms | 5.8ms | 63.2MB | [Link](https://drive.google.com/file/d/14-SsvoaTl-esC3JOzomHDnI9OGgdO2OR/view?usp=drive_link) |
| EfficientViT-L0 | 1s | 31.6ms | 6ms | 117.5MB | - |
Zero-Shot Instance Segmentation on COCO2017 validation dataset
| Image Encoder | mAP<sup>mask<br>50-95 | mIoU (all) | mIoU (large) | mIoU (medium) | mIoU (small) |
| --------------- | :-------------------: | :--------: | :----------: | :-----------: | :----------: |
| ResNet18 | - | 70.6 | 79.6 | 73.8 | 62.4 |
| MobileSAM | - | 72.8 | 80.4 | 75.9 | 65.8 |
| PPHGV2-B1 | 41.2 | 75.6 | 81.2 | 77.4 | 70.8 |
| PPHGV2-B2 | 42.6 | 76.5 | 82.2 | 78.5 | 71.5 |
| PPHGV2-B4 | 44.0 | 77.3 | 83.0 | 79.7 | 72.1 |
| EfficientViT-L0 | 45.6 | 78.6 | 83.7 | 81.0 | 73.3 |
## Usage
```python3
from nanosam.utils.predictor import Predictor
image_encoder_cfg = {
"path": "data/sam_hgv2_b4_ln_nonorm_image_encoder.onnx",
"name": "OnnxModel",
"provider": "cpu",
"normalize_input": False,
}
mask_decoder_cfg = {
"path": "data/efficientvit_l0_mask_decoder.onnx",
"name": "OnnxModel",
"provider": "cpu",
}
predictor = Predictor(encoder_cfg, decoder_cfg)
image = PIL.Image.open("assets/dogs.jpg")
predictor.set_image(image)
mask, _, _ = predictor.predict(np.array([[x, y]]), np.array([1]))
```
The point labels may be
| Point Label | Description |
| :---------: | ------------------------- |
| 0 | Background point |
| 1 | Foreground point |
| 2 | Bounding box top-left |
| 3 | Bounding box bottom-right |
|