In this tutorial, we will walk you through the process of training U-NET on an original dataset. We will cover the basics of U-NET architecture and dive into the details of dataset preparation, including data augmentation techniques. You will learn how to implement U-NET from scratch, as well as how to fine-tune an existing pre-trained model. We will also share some tips and tricks for optimizing model performance and improving segmentation accuracy. By the end of this tutorial, you will have a solid understanding of how to train U-NET on your own dataset and apply it to a variety of real-world image segmentation tasks. Check out the references below for more information on U-NET tutorials that cover related topics.

⭐ U-NET Segmentation Course: [ Ссылка ]
⭐ JOIN our Membership to get access to Source Code: [ Ссылка ]

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