tripletnet's Introduction

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:

CIFAR10/100: https://github.com/soumith/cifar.torch.git
STL10: https://github.com/eladhoffer/stl10.torch
SVHN: https://github.com/torch/tutorials/blob/master/A_datasets/svhn.lua
MNIST: https://github.com/andresy/mnist

##Dependencies

Torch (http://torch.ch)
"eladtools" (https://github.com/eladhoffer/eladtools) for optimizer.
"nngraph" (https://github.com/torch/nngraph) for TripletNet configuration.
"cudnn.torch" (https://github.com/soumith/cudnn.torch) for faster training. Can be avoided by changing "cudnn" to "nn" in models.

##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)

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