rwightman/pytorch-image-models
Pytorch Image Models
This project is a library of pretrained computer vision architectures and backbones for image classification and feature extraction. It serves as a comprehensive model zoo and collection of standardized image encoders, including ResNet, Vision Transformers, and EfficientNet, for use in visual analysis and as backbones for object detection and image segmentation.
The library provides a framework for distributed training and evaluation of image models using advanced data augmentation and optimization scripts. It includes a dedicated toolset for converting trained PyTorch vision models into the ONNX format to enable cross-platform deployment and inference.
The system covers high-level capabilities for model development, including multi-scale feature extraction, classifier head management, and the ability to handle images with variable dimensions. Training infrastructure is provided for distributed GPU environments, incorporating learning rate scheduling and stochastic augmentation techniques to improve model robustness and convergence.
Features
- Computer Vision Models - Provides a comprehensive collection of neural network architectures designed for image classification and feature extraction.
- Image Classification Models - Offers a wide array of pretrained architectures specifically designed for image classification and visual recognition tasks.
- Pretrained Model Integrations - Provides utilities for loading and integrating specialized pretrained vision models to accelerate training.
- Deep Learning Training Pipelines - Offers end-to-end workflows for high-performance distributed training and evaluation of image encoders.
- Distributed Training - Provides frameworks and utilities for scaling model training across multiple GPUs and nodes.
- Distributed Training Runtimes - Offers infrastructure support for executing high-performance training and inference across multi-GPU clusters.
- Image Encoder Embedding Extractions - Provides standardized image encoders that extract numerical vector representations to serve as backbones for detection and segmentation.
- Vision Model Training - Ships a framework for distributed training and evaluation of image models using advanced data augmentation.
- Pretrained Weight Initializers - Provides utilities for loading pretrained weights to accelerate convergence and avoid training from scratch.
- Computer Vision Features - Provides methods for extracting visual patterns and intermediate layers to create feature maps for detection tasks.
- Classifier Head Management - Provides tools to adjust or reset the classification head to adapt backbones for different class counts.
- Image Augmentation - Provides methods for increasing dataset diversity through random image transformations like Mixup and CutMix.
- Computer Vision Training - Provides optimized training routines and scripts specifically for image-based neural network architectures.
- Data Augmentation Techniques - Integrates specific data augmentation methods like pixel-level mixing and cropping to improve model robustness.
- Feature Extraction Pipelines - Implements tools for extracting multi-scale feature maps from neural network backbones for downstream tasks.
- ONNX Model Exporters - Provides utilities for converting trained PyTorch models into the standardized ONNX format.
- Modular Backbone Architectures - Employs neural network structures designed as decoupled feature extractors for flexible adaptation.
- Advanced Learning - Collection of state-of-the-art image models and training recipes.
- Computer Vision - Collection of state-of-the-art pretrained image models and scripts.
- Computer Vision Models - Comprehensive collection of image models, scripts, and pretrained weights.
- Model Architectures - Collection of state-of-the-art image models and pretrained weights.
- Model Implementations - Collection of state-of-the-art image models and pretrained weights.
- Neural Network Architectures - Collection of high-performance image classification models and weights.
- PyTorch Ecosystem - Collection of state-of-the-art image models.
- Convolutional Neural Networks (CNNs) - Listed in the “Convolutional Neural Networks (CNNs)” section of the The Incredible Pytorch awesome list.
Star history
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