Notice: This repo is no longer actively maintained. You are very welcome to use it, but I am unable to respond to issues and provide support.

This repo contains a collection of common MatConvNet functions and DagNN layers which are shared across a number of classification and object detection frameworks.

• vl_nnmax - element-wise maximum across tensors
• vl_nnsum - element-wise sum across tensors
• vl_nninterp - a wrapper for bilinear interpolation
• vl_nnslice - slicing along a given dimension
• vl_nnspatialsoftmax - spatial application of the softmax operator
• vl_nnreshape - tensor reshaping
• vl_nnchannelshuffle - channel shuffling (introduced in ShuffleNet)
• vl_nnflatten - flatten along a given dimension
• vl_nnglobalpool - global pooling
• vl_nnsoftmaxt - softmax along a given dimension
• vl_nncrop_wrapper - autonn function wrapper for vl_nncrop.m
• vl_nnaxpy - vector op y <- a*x + y (BLAS Level One style naming convention)
• vl_nngnorm - group normalization (an alternative to batch norm)
• vl_nnhuberloss - computation of the Huber (L1-smooth) loss
• vl_nneuclidenaloss - computation of the Euclidean (L2-smooth) loss
• vl_nntukeyloss - computation of Tukey's Biweight (robust) loss
• vl_nnsoftmaxceloss - soft-target cross entropy loss (operates on logits)
• vl_nncaffepool - "caffe-style" pooling (applies padding before pooling kernel)
• vl_nnl2norm - l2 feature normalisation

mcnExtraLayers requires the following modules:

• autonn - automatic differentiation

The module also contains some additional utilities which may be useful during network training:

• findBestCheckpoint - function to rank and prune network checkpoints saved during training (useful for saving space automatically at the end of a training run
• checkLearningParams - compare mcn network against a caffe prototxt

The module is easiest to install with the vl_contrib package manager:

vl_contrib('install', 'mcnExtraLayers') ;
vl_contrib('setup', 'mcnExtraLayers') ;
vl_contrib('test', 'mcnExtraLayers') ; % optional