Deci-AI/super-gradients
Super Gradients
Super-Gradients este un framework de computer vision PyTorch și o bibliotecă de antrenare concepută pentru întregul ciclu de viață al modelelor de viziune. Funcționează ca un optimizator de modele deep learning și un toolkit de implementare pentru antrenarea și fine-tuning-ul modelelor în sarcini de clasificare a imaginilor, detectare a obiectelor, segmentare semantică și estimare a posturii.
Proiectul oferă instrumente specifice pentru optimizarea modelelor, inclusiv distilarea cunoștințelor teacher-student și compresia preciziei numerice pentru a reduce cerințele de memorie și calcul. Include, de asemenea, implementarea arhitecturii Yolo-NAS pentru detectarea obiectelor de înaltă performanță.
Framework-ul acoperă o suprafață largă de capabilități, inclusiv antrenarea distribuită pe GPU, pipeline-uri de viziune modulare și automatizarea rulărilor de antrenare prin configurații de rețetă structurate. Mai mult, gestionează încărcarea datelor, augmentarea imaginilor și exportul ponderilor antrenate în formate universale pentru acceleratoare hardware de producție.
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.
