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README.md

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-license: gpl-3.0
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+license: gpl-3.0
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+![](https://aiot.aidlux.com/_next/image?url=%2Fapi%2Fv1%2Ffiles%2Fmodel%2Fcover%2F%25E5%259B%25BE-12.png&w=640&q=75)
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+## U-Net: Semantic Segmentation
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+U-Net is a convolutional neural network designed for biomedical image segmentation, introduced by Olaf Ronneberger et al. in 2015. The model gets its name from its U-shaped architecture, featuring a symmetrical encoder-decoder structure. The encoder part extracts features from the image through a series of convolutions and downsampling operations, while the decoder part restores the spatial resolution through upsampling, combining the extracted features to accurately locate and segment objects within the image. U-Net uses skip connections that pass feature maps from the encoder directly to the decoder, aiding in the recovery of fine details. This design makes U-Net highly effective for tasks requiring precise localization, such as medical image segmentation, and it is widely applied in other areas like remote sensing, autonomous driving, and image denoising.
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+### Source model
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+- Input shape: 640x1280
+- Number of parameters: 29.6M
+- Model size: 118.4M
+- Output shape: 1x2x640x1280
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+Source model repository: [U-Net](https://github.com/milesial/Pytorch-UNet)
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+## Performance Reference
+
+Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models)
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+## Inference & Model Conversion
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+Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models)
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+## License
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+- Source Model: [GPL-3.0](https://github.com/milesial/Pytorch-UNet/blob/master/LICENSE)
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+- Deployable Model: [GPL-3.0](https://github.com/milesial/Pytorch-UNet/blob/master/LICENSE)