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README.md

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-license: other
-license_name: aplux-model-farm-license
-license_link: https://aiot.aidlux.com/api/v1/files/license/model_farm_license_en.pdf
----
+---
+license: other
+license_name: aplux-model-farm-license
+license_link: https://aiot.aidlux.com/api/v1/files/license/model_farm_license_en.pdf
+pipeline_tag: image-segmentation
+tags:
+- AIoT
+- QNN
+---
+
+![](https://aiot.aidlux.com/_next/image?url=%2Fapi%2Fv1%2Ffiles%2Fmodel%2Fcover%2F20250320025512_%25E5%259B%25BE1(10).png&w=640&q=75)
+
+## SAM2-Unet-tiny: Semantic Segmentation
+
+SAM2-Unet is a hybrid segmentation model integrating the Segment Anything Model (SAM) with U-Net, optimized for medical image segmentation and few-shot learning. It incorporates SAM's visual prompt mechanism into U-Net's encoder-decoder structure, enabling dynamic target guidance via interactive point/box inputs while retaining skip connections for multi-scale feature fusion. Lightweight adapters fine-tune SAM's pretrained weights to enhance sensitivity to low-contrast regions in medical images (e.g., CT/MRI) and reduce reliance on large annotated datasets. Supporting zero-shot transfer and few-shot tuning, it improves Dice scores by ~8% over traditional U-Net on BraTS and ISIC benchmarks with low computational overhead, ideal for clinical diagnostics and real-time lesion localization.
+
+### Source model
+
+- Input shape: 1x3x352x352
+- Number of parameters: 28.38M
+- Model size: 119.42M
+- Output shape: 1x1x352x352
+
+The source model can be found [here](https://github.com/WZH0120/SAM2-UNet)
+
+## Performance Reference
+
+Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models)
+
+## Inference & Model Conversion
+
+Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models)
+
+## License
+
+- Source Model: [APACHE-2.0](https://github.com/WZH0120/SAM2-UNet/blob/main/LICENSE)
+
+- Deployable Model: [APLUX-MODEL-FARM-LICENSE](https://aiot.aidlux.com/api/v1/files/license/model_farm_license_en.pdf)