Skip to content

chenyaofo/pytorch-cifar-models

Repository files navigation

PyTorch CIFAR Models

Introduction

The goal of this project is to provide some neural network examples and a simple training codebase for begginners.

Get Started with Google Colab Open In Colab

Train Models: Open the notebook to train the models from scratch on CIFAR10/100. It will takes several hours depend on the complexity of the model and the allocated GPU type.

Test Models: Open the notebook to measure the validation accuracy on CIFAR10/100 with pretrained models. It will only take about few seconds.

Use Models with Pytorch Hub

You can simply use the pretrained models in your project with torch.hub API. It will automatically load the code and the pretrained weights from GitHub (If you cannot directly access GitHub, please check this issue for solution).

import torch
model = torch.hub.load("chenyaofo/pytorch-cifar-models", "cifar10_resnet20", pretrained=True)

To list all available model entry, you can run:

import torch
from pprint import pprint
pprint(torch.hub.list("chenyaofo/pytorch-cifar-models", force_reload=True))

Model Zoo

CIFAR-10

Model Top-1 Acc.(%) Top-5 Acc.(%) #Params.(M) #MAdds(M)
resnet20 92.60 99.81 0.27 40.81 model | log
resnet32 93.53 99.77 0.47 69.12 model | log
resnet44 94.01 99.77 0.66 97.44 model | log
resnet56 94.37 99.83 0.86 125.75 model | log
vgg11_bn 92.79 99.72 9.76 153.29 model | log
vgg13_bn 94.00 99.77 9.94 228.79 model | log
vgg16_bn 94.16 99.71 15.25 313.73 model | log
vgg19_bn 93.91 99.64 20.57 398.66 model | log
mobilenetv2_x0_5 92.88 99.86 0.70 27.97 model | log
mobilenetv2_x0_75 93.72 99.79 1.37 59.31 model | log
mobilenetv2_x1_0 93.79 99.73 2.24 87.98 model | log
mobilenetv2_x1_4 94.22 99.80 4.33 170.07 model | log
shufflenetv2_x0_5 90.13 99.70 0.35 10.90 model | log
shufflenetv2_x1_0 92.98 99.73 1.26 45.00 model | log
shufflenetv2_x1_5 93.55 99.77 2.49 94.26 model | log
shufflenetv2_x2_0 93.81 99.79 5.37 187.81 model | log
repvgg_a0 94.39 99.82 7.84 489.08 model | log
repvgg_a1 94.89 99.83 12.82 851.33 model | log
repvgg_a2 94.98 99.82 26.82 1850.10 model | log

CIFAR-100

Model Top-1 Acc.(%) Top-5 Acc.(%) #Params.(M) #MAdds(M)
resnet20 68.83 91.01 0.28 40.82 model | log
resnet32 70.16 90.89 0.47 69.13 model | log
resnet44 71.63 91.58 0.67 97.44 model | log
resnet56 72.63 91.94 0.86 125.75 model | log
vgg11_bn 70.78 88.87 9.80 153.34 model | log
vgg13_bn 74.63 91.09 9.99 228.84 model | log
vgg16_bn 74.00 90.56 15.30 313.77 model | log
vgg19_bn 73.87 90.13 20.61 398.71 model | log
mobilenetv2_x0_5 70.88 91.72 0.82 28.08 model | log
mobilenetv2_x0_75 73.61 92.61 1.48 59.43 model | log
mobilenetv2_x1_0 74.20 92.82 2.35 88.09 model | log
mobilenetv2_x1_4 75.98 93.44 4.50 170.23 model | log
shufflenetv2_x0_5 67.82 89.93 0.44 10.99 model | log
shufflenetv2_x1_0 72.39 91.46 1.36 45.09 model | log
shufflenetv2_x1_5 73.91 92.13 2.58 94.35 model | log
shufflenetv2_x2_0 75.35 92.62 5.55 188.00 model | log
repvgg_a0 75.22 92.93 7.96 489.19 model | log
repvgg_a1 76.12 92.71 12.94 851.44 model | log
repvgg_a2 77.18 93.51 26.94 1850.22 model | log