Swin-Transformer is a deep learning framework designed for training and deploying hierarchical vision transformer models. It serves as a research library and toolkit for computer vision tasks, providing the infrastructure to build models that replace standard convolution operations with sliding window self-attention mechanisms. By utilizing a multi-scale feature hierarchy, the framework enables the processing of visual data at varying resolutions and spatial scales.
The project distinguishes itself through its implementation of shifted window partitioning, which facilitates global information flow across image patches while maintaining linear computational complexity. It supports advanced scaling techniques, including mixture-of-experts architectures, to increase model capacity without a proportional rise in inference costs. These capabilities are complemented by a robust suite of tools for self-supervised representation learning, allowing for the extraction of visual features from unlabeled data.
The framework provides comprehensive support for distributed deep learning, enabling the parallelization of training across multiple graphics cards and compute nodes. It includes built-in optimizations such as mixed precision training and gradient checkpointing to manage memory consumption and accelerate throughput during large-scale experiments. Users can also perform fine-tuning on pre-trained models, apply feature distillation, and manage complex training schedules through configurable hyperparameters.
The repository includes scripts and configuration utilities to support image classification, object detection, and semantic segmentation workflows. It is designed to be installed as a Python-based library, offering a modular approach to defining model architectures and executing distributed training routines.
