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)