Update docs to PyT 1.13

README.md

@@ -1,7 +1,7 @@
 # U-Net: Semantic segmentation with PyTorch
 <a href="#"><img src="https://img.shields.io/github/actions/workflow/status/milesial/PyTorch-UNet/main.yml?logo=github&style=for-the-badge" /></a>
 <a href="https://hub.docker.com/r/milesial/unet"><img src="https://img.shields.io/badge/docker%20image-available-blue?logo=Docker&style=for-the-badge" /></a>
-<a href="https://pytorch.org/"><img src="https://img.shields.io/badge/PyTorch-v1.12+-red.svg?logo=PyTorch&style=for-the-badge" /></a>
+<a href="https://pytorch.org/"><img src="https://img.shields.io/badge/PyTorch-v1.13+-red.svg?logo=PyTorch&style=for-the-badge" /></a>
 <a href="#"><img src="https://img.shields.io/badge/python-v3.6+-blue.svg?logo=python&style=for-the-badge" /></a>
 
 ![input and output for a random image in the test dataset](https://i.imgur.com/GD8FcB7.png)
@@ -27,7 +27,7 @@ Customized implementation of the [U-Net](https://arxiv.org/abs/1505.04597) in Py
 
 1. [Install CUDA](https://developer.nvidia.com/cuda-downloads)
 
-2. [Install PyTorch 1.12 or later](https://pytorch.org/get-started/locally/)
+2. [Install PyTorch 1.13 or later](https://pytorch.org/get-started/locally/)
 
 3. Install dependencies
 ```bash