wqiiqw / tensorflow_resnet_cifar10

Proper tensorflow implementation of ResNet-s for CIFAR10 dataset corresponding to the original paper.

tensorflow_resnet_cifar10

This repositories contains an implementation of the original ResNet paper with tensorflow 2 and keras on the CIFAR10 dataset.

Requirements

Before running the training be sure the following python libraries are installed:

• tensorflow 2.3.1 or higher
• matplotlib 3.3.2 or higher
• numpy 1.18.5 or higher
• scikit-image 0.16.2 or higher

Run

Before training, the CIFAR10 dataset needs to be converted into tfrecord files. To do so, please use the following command by replacing the path/to/cifar10 with the appropriate location:

python prepare_data.py --data_path='/path/to/cifar10'

The training of all the resnets can be run with:

python train.py

If you want to train only a particular ResNet or change the training hyperparameters, please edit the global variables defined in the beginning of train.py.

Logs

During training, this implementation will store regularly:

• the keras model
• the tensorboard logs
• images of the model predictions on a batch of test samples

Performance

The performances below were obtained by doing only one run on all the model and taking the best test error during training. With model selection, the test errors should undoubtedly improve.

Name	# layers	# params	Test err(paper)	Test err(this impl.)
ResNet20	20	0.27M	8.75%	8.68%
ResNet32	32	0.46M	7.51%	7.69%
ResNet44	44	0.66M	7.17%	7.31%
ResNet56	56	0.85M	6.97%	7.04%
ResNet110	110	1.7M	6.43%	6.75%
ResNet120	1202	19.4M	7.93%	7.33%

Acknowledgement

This code is inspired by the two following repositories:

• pytorch_resnet_cifar10
• tf-resnet-cifar10