CapsNet-Pytorch

A Pytorch implementation of CapsNet in the paper:
Sara Sabour, Nicholas Frosst, Geoffrey E Hinton. Dynamic Routing Between Capsules. NIPS 2017
The current average test error = 0.34% and best test error = 0.30%.

Differences with the paper:

• We use the learning rate decay with decay factor = 0.9 and step = 1 epoch,
  while the paper did not give the detailed parameters (or they didn't use it?).
• We only report the test errors after 50 epochs training.
  In the paper, I suppose they trained for 1250 epochs according to Figure A.1?
• We use MSE (mean squared error) as the reconstruction loss and the coefficient for the loss is lam_recon=0.0005*784=0.392.
  This should be equivalent to using SSE (sum squared error) and lam_recon=0.0005 as in the paper.

TODO

• Conduct experiments on other datasets.
• Explore interesting characteristics of CapsuleNet.

Contacts

• Your contributions to the repo are always welcome. Open an issue or contact me with E-mail guoxifeng1990@163.com or WeChat wenlong-guo.

Usage

Step 1. Install Pytorch from source

I'm using the source code of Nov 24, 2017. The newest code should be working too.
Go https://github.com/pytorch/pytorch for installation instructions.

Step 2. Clone this repository to local.

git clone https://github.com/XifengGuo/CapsNet-Pytorch.git
cd CapsNet-Pytorch

Step 3. Train a CapsNet on MNIST

Training with default settings:

python capsulenet.py

Launching the following command for detailed usage:

python capsulenet.py -h

Step 4. Test a pre-trained CapsNet model

Suppose you have trained a model using the above command, then the trained model will be saved to result/trained_model.pkl. Now just launch the following command to get test results.

python capsulenet.py --testing --weights result/trained_model.pkl

It will output the testing accuracy and show the reconstructed images. The testing data is same as the validation data. It will be easy to test on new data, just change the code as you want.

You can also just download a model I trained from https://pan.baidu.com/s/1dFLFtT3

Results

Test Errors

CapsNet classification test error on MNIST. Average and standard deviation results are reported by 3 trials. The results can be reproduced by launching the following commands.

python capsulenet.py --routings 1 #CapsNet-v1   
python capsulenet.py --routings 3 #CapsNet-v2

Method	Routing	Reconstruction	MNIST (%)	Paper
Baseline	--	--	--	0.39
CapsNet-v1	1	yes	0.36 (0.016)	0.29 (0.011)
CapsNet-v2	3	yes	0.34 (0.029)	0.25 (0.005)

Losses and accuracies:

Training Speed

About 73s / epoch on a single GTX 1070 GPU.
About 43s / epoch on a single GTX 1080Ti GPU.

Reconstruction result

The result of CapsNet-v2 by launching

python capsulenet.py --testing --weights result/trained_model.pkl

Digits at top 5 rows are real images from MNIST and digits at bottom are corresponding reconstructed images.

Other Implementations

• Keras:

  • XifengGuo/CapsNet-Keras

• TensorFlow:

  • naturomics/CapsNet-Tensorflow
    I referred to some functions in this repository.
  • InnerPeace-Wu/CapsNet-tensorflow
  • chrislybaer/capsules-tensorflow

• PyTorch:

  • timomernick/pytorch-capsule
  • gram-ai/capsule-networks
  • nishnik/CapsNet-PyTorch
  • leftthomas/CapsNet

• MXNet:

  • AaronLeong/CapsNet_Mxnet

• Chainer:

  • soskek/dynamic_routing_between_capsules

• Matlab:

  • yechengxi/LightCapsNet