resnet

Reproduced ResNet on CIFAR-10 and CIFAR-100 dataset.

contact: panyx0718 ([email protected])

Dataset:

https://www.cs.toronto.edu/~kriz/cifar.html

Related papers:

Identity Mappings in Deep Residual Networks

https://arxiv.org/pdf/1603.05027v2.pdf

Deep Residual Learning for Image Recognition

https://arxiv.org/pdf/1512.03385v1.pdf

Wide Residual Networks

https://arxiv.org/pdf/1605.07146v1.pdf

Settings:

• Random split 50k training set into 45k/5k train/eval split.
• Pad to 36x36 and random crop. Horizontal flip. Per-image whitenting.
• Momentum optimizer 0.9.
• Learning rate schedule: 0.1 (40k), 0.01 (60k), 0.001 (>60k).
• L2 weight decay: 0.002.
• Batch size: 128. (28-10 wide and 1001 layer bottleneck use 64)

Results:

 

CIFAR-10 Model	Best Precision	Steps
32 layer	92.5%	~80k
110 layer	93.6%	~80k
164 layer bottleneck	94.5%	~80k
1001 layer bottleneck	94.9%	~80k
28-10 wide	95%	~90k

CIFAR-100 Model	Best Precision	Steps
32 layer	68.1%	~45k
110 layer	71.3%	~60k
164 layer bottleneck	75.7%	~50k
1001 layer bottleneck	78.2%	~70k
28-10 wide	78.3%	~70k

Prerequisite:

1. Install TensorFlow, Bazel.

2. Download CIFAR-10/CIFAR-100 dataset.

curl -o cifar-10-binary.tar.gz https://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz
curl -o cifar-100-binary.tar.gz https://www.cs.toronto.edu/~kriz/cifar-100-binary.tar.gz

How to run:

# cd to the your workspace.
# It contains an empty WORKSPACE file, resnet codes and cifar10 dataset.
# Note: User can split 5k from train set for eval set.
ls -R
  .:
  cifar10  resnet  WORKSPACE

  ./cifar10:
  data_batch_1.bin  data_batch_2.bin  data_batch_3.bin  data_batch_4.bin
  data_batch_5.bin  test_batch.bin

  ./resnet:
  BUILD  cifar_input.py  g3doc  README.md  resnet_main.py  resnet_model.py

# Build everything for GPU.
bazel build -c opt --config=cuda resnet/...

# Train the model.
bazel-bin/resnet/resnet_main --train_data_path=cifar10/data_batch* \
                             --log_root=/tmp/resnet_model \
                             --train_dir=/tmp/resnet_model/train \
                             --dataset='cifar10' \
                             --num_gpus=1

# While the model is training, you can also check on its progress using tensorboard:
tensorboard --logdir=/tmp/resnet_model

# Evaluate the model.
# Avoid running on the same GPU as the training job at the same time,
# otherwise, you might run out of memory.
bazel-bin/resnet/resnet_main --eval_data_path=cifar10/test_batch.bin \
                             --log_root=/tmp/resnet_model \
                             --eval_dir=/tmp/resnet_model/test \
                             --mode=eval \
                             --dataset='cifar10' \
                             --num_gpus=0