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

Deprecated

This code is not actively maintained. Please use the offical ResNet implementation instead.

Xin Pan

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 whitening.

• 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 models repository and run with bash. Expected command output shown.# The directory should contain an empty WORKSPACE file, the resnet code, and the cifar10 dataset.# Note: The 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

链接：https://github.com/tensorflow/models/tree/master/research/resnet