chenyaofo / pytorch-cifar-models

Pretrained models on CIFAR10/100 in PyTorch

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

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