  Image Segmentation plays an essential role in computer vision and image
processing with various applications from medical diagnosis to autonomous car
driving. A lot of segmentation algorithms have been proposed for addressing
specific problems. In recent years, the success of deep learning techniques has
tremendously influenced a wide range of computer vision areas, and the modern
approaches of image segmentation based on deep learning are becoming prevalent.
In this article, we introduce a high-efficient development toolkit for image
segmentation, named PaddleSeg. The toolkit aims to help both developers and
researchers in the whole process of designing segmentation models, training
models, optimizing performance and inference speed, and deploying models.
Currently, PaddleSeg supports around 20 popular segmentation models and more
than 50 pre-trained models from real-time and high-accuracy levels. With
modular components and backbone networks, users can easily build over one
hundred models for different requirements. Furthermore, we provide
comprehensive benchmarks and evaluations to show that these segmentation
algorithms trained on our toolkit have more competitive accuracy. Also, we
provide various real industrial applications and practical cases based on
PaddleSeg. All codes and examples of PaddleSeg are available at
https://github.com/PaddlePaddle/PaddleSeg.
