weiaicunzai/pytorch-cifar100
Pytorch Cifar100
This is a PyTorch image classification framework designed for training and evaluating convolutional neural networks. It provides a comprehensive library of pre-defined architectures and a training pipeline specifically implemented for the CIFAR-100 benchmark dataset.
The framework includes a variety of convolutional neural network implementations, ranging from standard research models to lightweight versions optimized for mobile devices. It features a modular model registry to initialize specific architectures and a benchmarking system to compare the effectiveness of different network designs.
The project covers deep learning training and evaluation workflows, incorporating state-checkpoint persistence, symmetry-based data augmentation, and learning rate warmup. It also includes utilities for measuring model accuracy by loading saved weights for predictive testing.
Features
- CIFAR-100 Benchmark Suites - Provides a complete set of tools for training and evaluating neural networks specifically on the CIFAR-100 benchmark dataset.
- Convolutional Neural Network Architectures - Implements a wide variety of convolutional neural network architectures, from research models to mobile-optimized versions.
- Deep Learning Training Pipelines - Provides an end-to-end deep learning training pipeline for initializing architectures, saving weights, and evaluating accuracy.
- Classification Training - Provides workflows for training image classification models with integrated warmup phases and automatic weight saving.
- PyTorch Training Frameworks - Ships a PyTorch training framework designed to benchmark the effectiveness of different network architectures on image data.
- Model Accuracy Evaluators - Includes tools for measuring the accuracy of trained neural networks against benchmark datasets using weight files.
- Pre-trained Weight Loading - Provides mechanisms for loading pre-trained weights into the model for predictive testing and evaluation.
- Learning Rate Warmup Strategies - Provides learning rate warmup strategies to stabilize initial gradient descent during the training phase.
- Model Architecture Registries - Features a modular model registry to instantiate various convolutional neural network architectures by name.
- Architecture Instantiation Systems - Provides a mapping system to programmatically instantiate various CNN architectures based on a specified name.
- Training Checkpoint Persistence - Includes state-checkpoint persistence to save model weights and optimizer states for recovery from interruptions.
- Random Crop and Flip Augmentations - Implements random cropping and flipping as symmetry-based data augmentation to prevent overfitting in image models.
- Project Utilities - PyTorch implementations of various image classification networks.
Star history
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