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Convolutional Neural Networks for CIFAR-10

This repository is about some implementations of CNN Architecture for cifar10.

cifar10

I just use Keras and Tensorflow to implementate all of these CNN models.
(maybe torch/pytorch version if I have time)
A pytorch version is available at CIFAR-ZOO

Requirements

  • Python (3.5)
  • keras (>= 2.1.5)
  • tensorflow-gpu (>= 1.4.1)

Architectures and papers

Documents & tutorials

There are also some documents and tutorials in doc & issues/3.
Get it if you need.
You can also see the articles if you can speak Chinese.

Accuracy of all my implementations

In particular：
Change the batch size according to your GPU's memory.
Modify the learning rate schedule may imporve the results of accuracy!

network GPU params batch size epoch training time accuracy(%)
Lecun-Network GTX1080TI 62k 128 200 30 min 76.23
Network-in-Network GTX1080TI 0.97M 128 200 1 h 40 min 91.63
Vgg19-Network GTX1080TI 39M 128 200 1 h 53 min 93.53
Residual-Network20 GTX1080TI 0.27M 128 200 44 min 91.82
Residual-Network32 GTX1080TI 0.47M 128 200 1 h 7 min 92.68
Residual-Network110 GTX1080TI 1.7M 128 200 3 h 38 min 93.93
Wide-resnet 16x8 GTX1080TI 11.3M 128 200 4 h 55 min 95.13
Wide-resnet 28x10 GTX1080TI 36.5M 128 200 10 h 22 min 95.78
DenseNet-100x12 GTX1080TI 0.85M 64 250 17 h 20 min 94.91
DenseNet-100x24 GTX1080TI 3.3M 64 250 22 h 27 min 95.30
DenseNet-160x24 1080 x 2 7.5M 64 250 50 h 20 min 95.90
ResNeXt-4x64d GTX1080TI 20M 120 250 21 h 3 min 95.19
SENet(ResNeXt-4x64d) GTX1080TI 20M 120 250 21 h 57 min 95.60

About LeNet and CNN training tips/tricks

LeNet is the first CNN network proposed by LeCun.
I used different CNN training tricks to show you how to train your model efficiently.

LeNet_keras.py is the baseline of LeNet,
LeNet_dp_keras.py used the Data Prepossessing [DP],
LeNet_dp_da_keras.py used both DP and the Data Augmentation[DA],
LeNet_dp_da_wd_keras.py used DP, DA and Weight Decay [WD]

network GPU DP DA WD training time accuracy(%)
LeNet_keras GTX1080TI - - - 5 min 58.48
LeNet_dp_keras GTX1080TI √ - - 5 min 60.41
LeNet_dp_da_keras GTX1080TI √ √ - 26 min 75.06
LeNet_dp_da_wd_keras GTX1080TI √ √ √ 26 min 76.23

For more CNN training tricks, see Must Know Tips/Tricks in Deep Neural Networks (by Xiu-Shen Wei)

About Learning Rate schedule

Different learning rate schedule may get different training/testing accuracy!
See ./htd, and HTD for more details.

About Multiple GPUs Training

Since the latest version of Keras is already supported keras.utils.multi_gpu_model, so you can simply use the following code to train your model with multiple GPUs:

from keras.utils import multi_gpu_model
from keras.applications.resnet50 import ResNet50

model = ResNet50()

# Replicates `model` on 8 GPUs.
parallel_model = multi_gpu_model(model, gpus=8)
parallel_model.compile(loss='categorical_crossentropy',optimizer='adam')

# This `fit` call will be distributed on 8 GPUs.
# Since the batch size is 256, each GPU will process 32 samples.
parallel_model.fit(x, y, epochs=20, batch_size=256)

About ResNeXt & DenseNet

Since I don't have enough machines to train the larger networks, I only trained the smallest network described in the paper. You can see the results in liuzhuang13/DenseNet and prlz77/ResNeXt.pytorch

   

Please feel free to contact me if you have any questions!

Citation

@misc{bigballon2017cifar10cnn,
  author = {Wei Li},
  title = {cifar-10-cnn: Play deep learning with CIFAR datasets},
  howpublished = {\url{https://github.com/BIGBALLON/cifar-10-cnn}},
  year = {2017}
}