Deep metric learning using Triplet network
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README.md

Deep Metric Learning Using Triplet Network

This code replicates the results from the paper “Deep metric learning using Triplet network” (http://arxiv.org/abs/1412.6622).

It can train a TripletNet on any of the {Cifar10/100, STL10, SVHN, MNIST} datasets.

Data

You can get the needed data using the following repos:

Dependencies

Models

Available models are at the “Models” directory. The basic Model.lua was used in the paper, while NiN based models achieve slightly better results.

Training

You can start training using:

th Main.lua -dataset Cifar10 -LR 0.1 -save new_exp_dir

Additional flags

Flag Default Value Description
modelsFolder ./Models/ Models Folder
network Model.lua Model file - must return valid network.
LR 0.1 learning rate
LRDecay 0 learning rate decay (in # samples
weightDecay 1e-4 L2 penalty on the weights
momentum 0.9 momentum
batchSize 128 batch size
optimization sgd optimization method
epoch -1 number of epochs to train (-1 for unbounded)
threads 8 number of threads
type cuda float or cuda
devid 1 device ID (if using CUDA)
load none load existing net weights
save time-identifier save directory
dataset Cifar10 Dataset - Cifar10, Cifar100, STL10, SVHN, MNIST
normalize 1 1 - normalize using only 1 mean and std values
whiten false whiten data
augment false Augment training data
preProcDir ./PreProcData/ Data for pre-processing (means,Pinv,P)