Deci-AI/super-gradients
Super Gradients
Super-Gradients is a PyTorch computer vision framework and training library designed for the full lifecycle of vision models. It functions as a deep learning model optimizer and a deployment toolkit for training and fine-tuning models across image classification, object detection, semantic segmentation, and pose estimation tasks.
The project provides specific tools for model optimization, including teacher-student knowledge distillation and numerical precision compression to reduce memory and computational requirements. It also includes the implementation of the Yolo-NAS architecture for high-performance object detection.
The framework covers a broad capability surface including distributed GPU training, modular vision pipelines, and the automation of training runs via structured recipe configurations. It further manages data loading, image augmentation, and the export of trained weights into universal formats for production hardware accelerators.
Features
- Computer Vision Training - Provides standardized training routines and scripts for image-based neural network architectures across multiple vision tasks.
- Vision Model Training - Provides a specialized framework for training and evaluating computer vision models, including classification, detection, and segmentation.
- Computer Vision Libraries - Provides a comprehensive library for managing datasets, augmentations, and training recipes for computer vision tasks.
- Distributed GPU Training - Provides techniques for distributing the computational load of neural network training across multiple graphics cards.
- Large-Scale Model Training - Implements specialized methodologies for scaling the training of large vision models across multiple GPUs.
- Computer Vision - Functions as a PyTorch-based toolkit for training, validating, and deploying deep learning models for computer vision.
- Modular Vision Pipelines - Implements architectures that decouple image processing, feature detection, and analysis stages into configurable, independent components.
- Deep Learning Optimization - Refines deep learning models by optimizing computational graphs and reducing model size for better execution efficiency.
- Teacher-Student Distillation - Implements teacher-student knowledge distillation to transfer patterns from large pre-trained models to smaller, efficient student models.
- Model Optimization Suites - Offers a suite of tools for model distillation and precision optimization to reduce memory and computational requirements.
- Mixed-Precision Quantization - Reduces memory and computational requirements by implementing numerical precision compression for deployment on resource-constrained hardware.
- PyTorch Computer Vision Pipelines - Provides end-to-end computer vision workflows and pipeline orchestration implemented using the PyTorch framework.
- Image Augmentations - Applies random transformations like scaling, cropping, and color jittering to training images to improve model generalization.
- Vision Dataset Loading - Includes utilities for importing and preparing standardized image and video databases for model training.
- Model Export Formats - Provides utilities for converting trained PyTorch models into standard industry formats for compatibility and deployment.
- Model Deployment Toolkits - Provides utilities to streamline the packaging and export of trained weights into formats compatible with hardware accelerators.
- Recipe Configuration Definitions - Defines training parameters and hyperparameters in structured YAML files for reproducible model training runs.
- Training Recipes - Provides standardized configurations and workflows for executing reproducible model training processes.
- YOLO Object Detectors - Implements the Yolo-NAS architecture for high-performance object detection.
- Pretrained Weight Initializers - Supports loading state-of-the-art pretrained weights to initialize model architectures and accelerate training convergence.
- Model Export Formats - Supports exporting trained models to interoperable formats for deployment on various runtimes and production environments.
- Computer Vision Frameworks - Library for training PyTorch-based computer vision models.
Star history
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