Reproduced ResNet on CIFAR-10 and CIFAR-100 dataset.
contact: panyx0718 (xpan@google.com)
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.
```shell
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:
```shell
# 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
```