  This paper introduces a novel segmentation framework that integrates a
classifier network with a reverse HRNet architecture for efficient image
segmentation. Our approach utilizes a ResNet-50 backbone, pretrained in a
semi-supervised manner, to generate feature maps at various scales. These maps
are then processed by a reverse HRNet, which is adapted to handle varying
channel dimensions through 1x1 convolutions, to produce the final segmentation
output. We strategically avoid fine-tuning the backbone network to minimize
memory consumption during training. Our methodology is rigorously tested across
several benchmark datasets including Mapillary Vistas, Cityscapes, CamVid,
COCO, and PASCAL-VOC2012, employing metrics such as pixel accuracy and mean
Intersection over Union (mIoU) to evaluate segmentation performance. The
results demonstrate the effectiveness of our proposed model in achieving high
segmentation accuracy, indicating its potential for various applications in
image analysis. By leveraging the strengths of both the ResNet-50 and reverse
HRNet within a unified framework, we present a robust solution to the
challenges of image segmentation.
