Architecture of the PSPNet
Architecture of PSPNet is little complex which is discussed below:
- Input and Feature Extraction
- The process begins with an input image, which undergoes feature extraction using a pretrained ResNet model with a dilated network strategy.
- The dilated network strategy helps extract detailed information from the image, and the final feature map size is 1/8 of the input image.
- Pyramid Pooling Module
- The Pyramid Pooling Module is introduced to gather contextual information on top of the extracted feature map.
- A 4-level pyramid is created, covering the entire image, half of the image, and small portions. These levels serve as a global prior for understanding the scene.
- The pooling kernels at different levels capture various contextual scales.
- The information from the pyramid is fused as the global prior and concatenated with the original feature map from the ResNet model.
- Final Prediction
- The concatenated information is then processed through a convolutional layer to generate the final prediction map.
- The convolutional layer refines the combined information, providing a detailed prediction of pixel-level scene parsing.
In short, the architecture leverages a pretrained model named ResNet with a dilated network strategy for feature extraction, enhances contextual understanding through Pyramid Pooling Module and efficiently generates pixel-level scene predictions.
PSPNet (Pyramid Scene Parsing Network) for Image Segmentation
Within the intricate landscape of semantic segmentation, the Pyramid Scene Parsing Network or PSPNet has emerged as a formidable architecture by showcasing unparalleled performance in deciphering intricate scenes. In this article, we will discuss about PSPNet and implement it.