Semantic segmentation plays a critical role in computer vision applications, enabling pixel-level understanding of images. The above model leverages an efficient spatial pyramid of dilated convolutions to address the challenges in this domain, providing superior performance with reduced computational complexity.
- Efficient Spatial Pyramid: The network utilizes a spatial pyramid of dilated convolutions to capture multi-scale context information effectively. This architecture improves the accuracy and robustness of semantic segmentation models.
This repository is organized as:
- training This directory contains the source code for training the ESPNet-C and ESPNet models.
- testing This directory contains the source code for evaluating the model on RGB Images.
The ESPNet-C model achieves mIoU of 70.0% on Cityscapes dataset. The ESPNet model achieves mIoU of 70.0% on Cityscapes dataset.
Output from Dataset 1
Output from Dataset 2
Output from Dataset 3