Gaiqi / pytorch_simple_classification_baselines

Simple pytorch classification baselines for MNIST, CIFAR and ImageNet

Pytorch simple classification baselines

This repository contains simple pytorch version of LeNet-5(MNIST), ResNet(CIFAR, ImageNet), AlexNet(ImageNet), VGG-16(CIFAR, ImageNet) baselines. There are both nn.DataParallel and nn.parallel.DistributedDataParallel version for multi GPU training, I highly recommand using nn.parallel.DistributedDataParallel since it's considerably faster than using nn.DataParallel.

Requirements:

• python>=3.5
• pytorch>=0.4.1(>=1.1.0 for DistributedDataParallel version)
• tensorboardX(optional)

Train

single GPU or multi GPU using nn.DataParallel

• python mnist_train_eval.py
• python cifar_train_eval.py
• python imgnet_train_eval.py

multi GPU using nn.parallel.DistributedDataParallel

• python -m torch.distributed.launch --nproc_per_node 2 cifar_train_eval.py --dist --gpus 0,1
• python -m torch.distributed.launch --nproc_per_node 2 imgnet_train_eval.py --dist --gpus 0,1

Results:

MNIST:

Model	Accuracy
LeNet-5	99.26%

CIFAR-10:

Model	Accuracy
ResNet-20	92.09%
ResNet-56	93.68%
VGG-16	93.99%

ImageNet2012:

Model	Top-1 Accuracy	Top-5 Accuracy
ResNet-18	69.67%	89.29%