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# UNet: semantic segmentation with PyTorch # UNet: semantic segmentation with PyTorch
[![xscode](https://img.shields.io/badge/Available%20on-xs%3Acode-blue?style=?style=plastic&logo=appveyor&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAEAAAABACAMAAACdt4HsAAAAGXRFWHRTb2Z0d2FyZQBBZG9iZSBJbWFnZVJlYWR5ccllPAAAAAZQTFRF////////VXz1bAAAAAJ0Uk5T/wDltzBKAAAAlUlEQVR42uzXSwqAMAwE0Mn9L+3Ggtgkk35QwcnSJo9S+yGwM9DCooCbgn4YrJ4CIPUcQF7/XSBbx2TEz4sAZ2q1RAECBAiYBlCtvwN+KiYAlG7UDGj59MViT9hOwEqAhYCtAsUZvL6I6W8c2wcbd+LIWSCHSTeSAAECngN4xxIDSK9f4B9t377Wd7H5Nt7/Xz8eAgwAvesLRjYYPuUAAAAASUVORK5CYII=)](https://xscode.com/milesial/Pytorch-UNet)
![input and output for a random image in the test dataset](https://framapic.org/OcE8HlU6me61/KNTt8GFQzxDR.png) ![input and output for a random image in the test dataset](https://framapic.org/OcE8HlU6me61/KNTt8GFQzxDR.png)
@ -92,6 +95,11 @@ Predicting images of 1918*1280 takes 1.5GB of memory.
Training takes much approximately 3GB, so if you are a few MB shy of memory, consider turning off all graphical displays. Training takes much approximately 3GB, so if you are a few MB shy of memory, consider turning off all graphical displays.
This assumes you use bilinear up-sampling, and not transposed convolution in the model. This assumes you use bilinear up-sampling, and not transposed convolution in the model.
## Support
Personalized support for issues with this repository, or integrating with your own dataset, available on [xs:code](https://xscode.com/milesial/Pytorch-UNet).
--- ---
Original paper by Olaf Ronneberger, Philipp Fischer, Thomas Brox: [https://arxiv.org/abs/1505.04597](https://arxiv.org/abs/1505.04597) Original paper by Olaf Ronneberger, Philipp Fischer, Thomas Brox: [https://arxiv.org/abs/1505.04597](https://arxiv.org/abs/1505.04597)