segmentation_models.pytorch

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![logo](https://i.ibb.co/dc1XdhT/Segmentation-Models-V2-Side-1-1.png)
**Python library with Neural Networks for Image Semantic
Segmentation based on [PyTorch](https://pytorch.org/).**


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The main features of the library are:

 - Super simple high-level API (just two lines to create a neural network)
 - 12 encoder-decoder model architectures (Unet, Unet++, Segformer, DPT, ...)
 - 800+ **pretrained** convolution- and transform-based encoders, including [timm](https://github.com/huggingface/pytorch-image-models) support
 - Popular metrics and losses for training routines (Dice, Jaccard, Tversky, ...)
 - ONNX export and torch script/trace/compile friendly

### 🤝 Sponsor: withoutBG

<a href="https://github.com/withoutbg/withoutbg" target="_blank">
  <img src="https://withoutbg.com/images/img-and-matte.png" alt="Sponsored by withoutBG"/>
</a>

[withoutBG](https://github.com/withoutbg/withoutbg) is a high-quality background removal tool. They built their open-source image matting and refiner models using `smp.Unet` and are proudly sponsoring this project.


### [📚 Project Documentation 📚](https://smp.readthedocs.io/)

Visit [Read The Docs Project Page](https://smp.readthedocs.io/) or read the following README to know more about Segmentation Models Pytorch (SMP for short) library

### 📋 Table of content
 1. [Quick start](#start)
 2. [Examples](#examples)
 3. [Models and encoders](#models-and-encoders)
 4. [Models API](#api)
    1. [Input channels](#input-channels)
    2. [Auxiliary classification output](#auxiliary-classification-output)
    3. [Depth](#depth)
 5. [Installa