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--- |
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license: gpl-3.0 |
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pipeline_tag: image-segmentation |
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tags: |
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- AIoT |
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- QNN |
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--- |
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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 |
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- Number of parameters: 29.6M |
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- Model size: 118.4M |
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- Output shape: 1x2x640x1280 |
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Source model repository: [U-Net](https://github.com/milesial/Pytorch-UNet) |
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## Performance Reference |
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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) |
