hyonzin/horovod-resnet-cifar10

TensorFlow implementation of ResNet-110 tested on CIFAR10

ResNet: Classifying CIFAR-10 images with Horovod

Note: this project is extension of tensorflow project github.com/chao-ji/tf-resnet-cifar10

• A lightweight TensorFlow implementation of ResNet model for classifying CIFAR-10 images.

• Reproduces the results presented in the paper.

• Shows the full schematic diagram of a 20-layer ResNet annotated with feature map sizes.

Usage

Clone the Repo

git clone [email protected]:hyonzin/horovod-resnet-cifar10.git

Download and untar CIFAR-10 dataset

wget https://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz
tar -xvzf cifar-10-binary.tar.gz

Train the ResNet Classifier

To train the ResNet model using default settings, simply run

python run_trainer.py \
  --data_path=cifar-10-batches-bin \
  --num_layers=110

To change the number of layers in the ResNet (for example, to 110), specify --num_layers=110. To degenerate the ResNet model to a Plain network, specify --shortcut_connections=False. To see a full list of arguments, run

python run_trainer.py --help

Evaluate a Trained ResNet Classifier

To evaluate the trained model on the test set (10,000 images), run

  python run_evaluator.py \
    --path=cifar-10-batches-bin \
    --ckpt_path=/PATH/TO/CKPT \
    --num_layers=110

Note that you need to specify the path to the checkpoint file containing trained weights via --ckpt_path.

References:

1. ResNet V1, Deep Residual Learning for Image Recognition, He et al.
2. ResNet V2, Identity Mappings in Deep Residual Networks, He et al.