LogosIntelligenceLab/SeaFormer

What it solves

SeaFormer addresses the challenge of running high-performance computer vision models on mobile devices. It provides a way to perform complex visual tasks like image classification and semantic segmentation while maintaining a balance between high accuracy and low latency.

How it works

SeaFormer uses a specialized architecture called a Squeeze-enhanced Axial Transformer. This design optimizes the way the model processes visual data by "squeezing" information to reduce computational overhead while using axial attention mechanisms to capture important spatial relationships in images. This makes the model lightweight enough for mobile hardware without sacrificing its ability to recognize and segment objects accurately.

Who it’s for

This project is for developers and researchers working on mobile computer vision applications who need efficient, high-speed models for tasks like scene understanding or image recognition.

Highlights

• Optimized for mobile devices with a focus on low latency and high performance.
• Supports both image classification and semantic segmentation tasks.
• Provides a range of model sizes (Tiny, Small, Base, Large) to suit different hardware constraints.
• Proven performance on standard benchmarks like ImageNet-1K, ADE20K, and Cityscapes.