About MatConvNet

MatConvNet was born in the Oxford Visual Geometry Group as an educational and research platform for fast prototyping of Convolutional Neural Networks.

MatConvNet is a flexible framework capable of learning large neural networks on one or multiple GPUs. Except for low-level building blocks, it is all written in MATLAB, which allows easy hacking, including creating new autodiff methods. Many research papers have used MatConvNet.

Changes

• 1.0-beta25 (August 2017).

  New features

  • Modular system vl_contrib giving quick access to many user contributions.

  Changes affecting backward compatibility

  The build system was simplified removing special cases for older MATLAB bugs and limitations, and to support C++11 as production language. Recent MATLAB, compilers, and CUDA versions should all still work.

• 1.0-beta24 (March 2017).

  New features

  • New toy example cnn_toy_data.m demonstrating using a customized imdb.
  • vl_argparse.m now supports dot paths and ignoring missing defaults.
  • Support for different example solvers (AdaGrad, Adam, AdaDelta, RMSProp) and ability to add new ones.
  • A new function vl_tshow.m to glance at tensors.
  • Bugfixes.

• 1.0-beta23 (September 2016).

  New features

  • A new function vl_nnroipool.m for region of interest pooling, supporting networks such as Fast-RCNN.
  • Imported Fast-RCNN models from Caffe.
  • An example Fast-RCNN implementation, training and testing.

• 1.0-beta22 (Spetember 2016).

  • Bugfixes.

• 1.0-beta21 (June 2016).

  New features

  • A new function vl_tacc.m to accumulate tensors efficiently.
  • A rewritten vl_imreadjpeg.m function that can load, jitter, and transfer images to the GPU in parallel.
  • A new function vl_tmove.m to transfer tensor data between multiple (local) MATLAB processes efficiently.
  • A wrapper ParameterSever.m to simplify the use of vl_tmove.m.
  • Adds support for ParameterSever in the examples.
  • Adds an option in the example training script to save the momentum between epochs.
  • Batch normalization can use CuDNN implementation.
  • vl_nnconv.m now supports the dilate option for dilated convolution.

  Changes affecting backward compatibility

  • The ImageNet example have been updated to use the new vl_imreadjpeg.m. This mainly affects the way images are loaded in getBatch.

  • The example scripts cnn_train.m and cnn_train_dag.m have been updated in various way, so that old snapshot files may not be compatible.

  • The way batch normalization accumulates moments during training has been changed slightly to work properly with complex architectures such as siamese ones where the number of data instances may change throughout the network.

• 1.0-beta20 (May 2016).

  New features

  • New spatial bilinear resampler vl_nnbilinearsampler.m to warp images spatially.

  • New cnn_stn_cluttered_mnist.m example to demonstrate spatial transformer networks.

  • MATLAB R2016a compatibility.

• 1.0-beta19 (April 2016).

  New features

  • Support for pre-trained ResNet models.

  • New Scale layer in DagNN.

  • Numerous improvements to DagNN.

  • Numerous refinements to example training scripts cnn_train.m and cnn_train_dag.m.

  • vl_nnpdist now can backpropagate both inputs.

  • CuDNN v5 support.

  • Improved the import-caffe.py script for compatibility with newer versions of Caffe.

• 1.0-beta18 (January 2016).

  New features

  • DOUBLE support. All vl_nn* commands now work with either DOUBLE or SINGLE (FLOAT) data types.

  • VL_IMREADJPEG() can now resize images.

  • More thorough unit testing and several bugfixes.

• 1.0-beta17 (December 2015).

  New features

  • Mac OS X 10.11 support. Since setting LD_LIBRARY_PATH is not supported under this OS due to security reasons, now MatConvNet binaries hardcode the location of the CUDA/cuDNN libraries as needed. This also simplifies starting up MATLAB.

  • This version changes slightly how cuDNN is configured; the cuDNN root directory is assumed to contain two subdirectories lib and include instead of the binary and include files directly. This matches how cuDNN is now distributed.

  • CuDNN v4 is now supported.

  • This version changes how batch normalization is handled. Now the average moments are learned together with the other parameters. The net result is that batch normalization is easy to bypass at test time (and implicitly done in validation, just like dropout).

  • The disableDropout parameter of vl_simplenn has been replaced by a more generic mode option that allows running in either normal mode or test mode. In the latter case, both dropout and batch normalization are bypassed. This is the same behavior of DagNN.mode.

  • Examples have been re-organized in subdirectories.

  • Compiles and works correctly with cuDNN v4. However, not all v4 features are used yet.

  • Adds an option to specify the maximum workspace size in the convolution routines using cuDNN.

  • The AlexNet, VGG-F, VGG-M, VGG-S examples provided in the examples/imagenet directory have been refined in order to produce deployable models. MatConvNet pretrained versions of these models are available for download.

  • A new option in vl_nnconv and vl_nnconvt allows setting the maximum amount of memory used by CuDNN to perform convolution.

  Changes affecting backward compatibility

  • This version changes slightly how SimpleNN networks should be handled. Use the vl_simplenn_tidy() to upgrade existing networks to the latest version of MatConvNet. This function is also useful to fill in missing default values for the parameters of the network layers. It is therefore also recommended to use vl_simplenn_tidy() when new models are defined.

  • The downloadable pre-trained models have been updated to match the new version of SimpleNN. The older models are still available for download. Note that old and new models are numerically equivalent, only the format is (slightly) different.

  • Recent versions of CuDNN may use by default a very large amount of memory for computation.

• 1.0-beta16 (October 2015). Adds VGG-Face as a pretrained model. Bugfixes.

• 1.0-beta15 (September 2015). Supports for new DagNN blocks and import script for the FCN models. Improved vl_nnbnorm.
• 1.0-beta14 (August 2015). New DagNN wrapper for networks with complex topologies. GoogLeNet support. Rewritten vl_nnloss block with support for more loss functions. New blocks, better documentation, bugfixes, new demos.
• 1.0-beta13 (July 2015). Much faster batch normalization and several minor improvements and bugfixes.
• 1.0-beta12 (May 2015). Added vl_nnconvt (convolution transpose or deconvolution).

• 1.0-beta11 (April 2015) Added batch normalization, spatial normalization, sigmoid, p-distance. Extended the example training code to support multiple GPUs. Significantly improved the tuning of the ImageNet and CIFAR examples. Added the CIFAR Network in Network model.

  This version changes slightly the structure of simplenn. In particular, the filters and biases fields in certain layers have been replaced by a weights cell array containing both tensors, simplifying a significant amount of code. All examples and downloadable models have been updated to reflect this change. Models using the old structure format still work but are deprecated.

  The cnn_train training code example has been rewritten to support multiple GPUs. The interface is nearly the same, but the useGpu option has been replaced by a gpus list of GPUs to use.

• 1.0-beta10 (March 2015) vl_imreadjpeg works under Windows as well.

• 1.0-beta9 (February 2015) CuDNN support. Major rewrite of the C/CUDA core.
• 1.0-beta8 (December 2014) New website. Experimental Windows support.
• 1.0-beta7 (September 2014) Adds VGG very deep models.
• 1.0-beta6 (September 2014) Performance improvements.
• 1.0-beta5 (September 2014) Bugfixes, adds more documentation, improves ImageNet example.
• 1.0-beta4 (August 2014) Further cleanup.
• 1.0-beta3 (August 2014) Cleanup.
• 1.0-beta2 (July 2014) Adds a set of standard models.
• 1.0-beta1 (June 2014) First public release.

Contributors

MatConvNet is developed by several hands:

• Andrea Vedaldi, project coordinator
• Karel Lenc, DaG, several building blocks and examples
• Sébastien Ehrhardt, GPU implementation of batch normalization, FCN building blocks and examples
• Ankush Gupta, spatial transformer implementation and examples
• Max Jaderberg, general improvements and bugfixes

MatConvNet quality also depends on the many people using the toolbox and providing us with feedback and bug reports.

Copyright

This package was originally created by Andrea Vedaldi and Karel Lenc and it is currently developed by a small community of contributors. It is distributed under the permissive BSD license (see also the file COPYING):

Copyright (c) 2014-16 The MatConvNet team.
All rights reserved.

Redistribution and use in source and binary forms are permitted
provided that the above copyright notice and this paragraph are
duplicated in all such forms and that any documentation,
advertising materials, and other materials related to such
distribution and use acknowledge that the software was developed
by the <organization>. The name of the <organization> may not be
used to endorse or promote products derived from this software
without specific prior written permission.  THIS SOFTWARE IS
PROVIDED ``AS IS'' AND WITHOUT ANY EXPRESS OR IMPLIED WARRANTIES,
INCLUDING, WITHOUT LIMITATION, THE IMPLIED WARRANTIES OF
MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE.

Acknowledgments

The implementation of the computational blocks in this library, and in particular of the convolution operators, is inspired by Caffe.

We gratefully acknowledge the support of NVIDIA Corporation with the donation of the GPUs used to develop this software.