Image Segmentation Keras : Implementation of Segnet, FCN, UNet, PSPNet and other models in Keras
This work offers a practical resource for computer vision researchers and practitioners working on semantic segmentation tasks, but it is incremental as it focuses on implementing existing models.
The paper presents a comprehensive library implementing popular semantic segmentation models like SegNet, FCN, UNet, and PSPNet in Keras, and evaluates and compares them on several datasets to provide a toolset for researchers and practitioners.
Semantic segmentation plays a vital role in computer vision tasks, enabling precise pixel-level understanding of images. In this paper, we present a comprehensive library for semantic segmentation, which contains implementations of popular segmentation models like SegNet, FCN, UNet, and PSPNet. We also evaluate and compare these models on several datasets, offering researchers and practitioners a powerful toolset for tackling diverse segmentation challenges.