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Modular toolboxes for developing and training deep learning architectures specifically for vision tasks using PyTorch.
Distinct from PyTorch Training Frameworks: Specializes PyTorch training frameworks specifically for the computer vision and multimodal domain.
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mmtracking is a PyTorch video perception framework designed for training and deploying computer vision models that analyze sequential image data. It provides specialized tools for multi-object tracking, video instance segmentation, and a configuration-driven system for managing deep learning models. The project utilizes a deep learning model registry and a configuration-driven pipeline to swap model backbones and detectors without modifying the core codebase. This modular approach allows for the development of custom perception architectures by combining various components and configurations.
Provides a modular PyTorch-based toolbox for developing and training deep learning architectures for video perception tasks.
mmpretrain is a modular PyTorch computer vision framework designed for developing, training, and benchmarking deep learning architectures. It serves as a comprehensive toolkit for vision tasks, providing a specialized platform for multimodal machine learning and self-supervised learning. The project features a computer vision model zoo containing architectural definitions and pre-trained weights for backbones such as ViT, ConvNeXt, and Swin Transformer. It distinguishes itself through a dedicated self-supervised learning toolkit that implements algorithms like MAE and DINO to train models wit
A modular PyTorch-based toolbox for developing, training, and benchmarking deep learning architectures for vision and multimodal tasks.