ResNet

imagenet-resnet.py, imagenet-resnet-se.py

Training code of ResNet on ImageNet, with pre-activation and squeeze-and-excitation. The pre-act ResNet follows the setup in fb.resnet.torch (except for the weight decay) and gets similar performance (with much fewer lines of code). Models can be downloaded here.

Model	Top 5 Error	Top 1 Error
ResNet18	10.55%	29.73%
ResNet34	8.51%	26.50%
ResNet50	7.24%	23.91%
ResNet50-SE	6.42%	22.94%
ResNet101	6.26%	22.53%

To train, just run:

./imagenet-resnet.py --data /path/to/original/ILSVRC --gpu 0,1,2,3 -d 50

You should be able to see good GPU utilization (around 95%), if your data is fast enough. See the tutorial on how to speed up your data.

load-resnet.py

This script only converts and runs ImageNet-ResNet{50,101,152} Caffe models released by Kaiming.

Example usage:

# convert caffe model to npy format
python -m tensorpack.utils.loadcaffe PATH/TO/{ResNet-101-deploy.prototxt,ResNet-101-model.caffemodel} ResNet101.npy
# run on an image
./load-resnet.py --load ResNet-101.npy --input cat.jpg --depth 101

The converted models are verified on ILSVRC12 validation set. The per-pixel mean used here is slightly different from the original.

Model	Top 5 Error	Top 1 Error
ResNet 50	7.89%	25.03%
ResNet 101	7.16%	23.74%
ResNet 152	6.81%	23.28%

cifar10-resnet.py

Reproduce pre-activation ResNet on CIFAR10.

Also see a DenseNet implementation of the paper Densely Connected Convolutional Networks.