UNet由编码器和解码器两部分组成,通过跳跃连接(skip connections)来融合不同层次的信息。基于PyTorch实现的UNet模型代码示例,如何使用该模型进行数据集测试的流程。 1. UNet模型定义 importtorchimporttorch.nnasnnimporttorch.nn.functionalasFclassDoubleConv(nn.Module):"""(convolution => [BN] => ReLU) * 2"""...
Our project does not depend on installing SAM2. If you have already configured an environment for SAM2, then directly using this environment should also be fine. You may also create a new conda environment: conda create -n sam2-unet python=3.10 conda activate sam2-unet pip install -r requ...
sam2unet.jpg test.py test.sh train.py train.sh 2 changes: 2 additions & 0 deletions 2 .gitattributes Original file line numberDiff line numberDiff line change @@ -0,0 +1,2 @@ # Auto detect text files and perform LF normalization * text=auto 201 changes: 201 additions & 0...
Existing models, such as traditional U-Net- and SAM-based architectures, either lack efficiency in handling class-specific segmentation or require extensive computational resources. This study aims to bridge this gap by proposing Segment Anything Model 2-UNetwork, a hybrid model that leverages the ...
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interpolate(self.head(x), scale_factor=4, mode='bilinear') return out, out1, out2 if __name__ == "__main__": with torch.no_grad(): model = SAM2UNet().cuda() x = torch.randn(1, 3, 352, 352).cuda() out, out1, out2 = model(x) print(out.shape, out1.shape, out2....
SAM2-UNet: Segment Anything 2 Makes Strong Encoder for Natural and Medical Image Segmentation - SAM2-UNet/requirements.txt at main · WZH0120/SAM2-UNet
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