README.md

---
license: gpl-3.0
pipeline_tag: image-segmentation
tags:
- AIoT
- QNN
---

![](https://aiot.aidlux.com/_next/image?url=%2Fapi%2Fv1%2Ffiles%2Fmodel%2Fcover%2F%25E5%259B%25BE-12.png&w=640&q=75)

## U-Net: Semantic Segmentation

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.

### Source model

- Input shape: 640x1280
- Number of parameters: 29.6M
- Model size: 118.4M
- Output shape: 1x2x640x1280

Source model repository: [U-Net](https://github.com/milesial/Pytorch-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: [GPL-3.0](https://github.com/milesial/Pytorch-UNet/blob/master/LICENSE)

- Deployable Model: [GPL-3.0](https://github.com/milesial/Pytorch-UNet/blob/master/LICENSE)