Segmentation Models :

• Segmentation models represent a crucial advancement in computer vision technology, designed to analyze images at the pixel level for precise object and feature identification. These sophisticated neural networks partition images into meaningful segments, enabling detailed understanding of image content by assigning specific classifications to each pixel. QpiAI pro has two SOTA segmentation models. These two segmentation models are based on instance segmentation.

• DeepLab V3+: DeepLab v3+ is an advanced semantic segmentation model architecture that improves upon its predecessor DeepLab v3 by incorporating key innovations in image segmentation. The model excels at precisely identifying and segmenting different objects within images at a pixel level.

• SegFormer (segformer-mit-b0)

  This lightweight variant of SegFormer uses the MiT-B0 (MixVision Transformer B0) backbone, offering a highly efficient architecture for segmentation with minimal resource demands. It captures multi-scale contextual features through hierarchical transformers, making it well-suited for real-time applications and embedded systems where speed is prioritized over ultra-high accuracy.

• SegFormer-Large (segformer-mit-b4)

  Powered by the MiT-B4 backbone, this SegFormer variant delivers a balanced trade-off between performance and precision. Its deeper architecture enhances global context and spatial detail, enabling more accurate segmentation while remaining computationally efficient. It’s ideal for use cases such as autonomous driving, urban scene understanding, and industrial automation, where segmentation accuracy is critical, but inference speed must be maintained.

• SeFormer-XLarge (se former-mit-b5)

  The most powerful variant in the SegFormer family, this model leverages the MiT-B5 backbone for state-of-the-art segmentation accuracy. It excels in capturing fine object boundaries and complex scene layouts, thanks to its rich hierarchical representation and deep transformer layers. Best suited for high-precision domains like medical image analysis, aerial imagery, and research-heavy computer vision tasks, where segmentation quality is paramount.