BYU CS classes

To gain experience coding a DNN architecture and learning program end-to-end, and to gain experience with Siamese network and ResNets.

For this lab, you will need to implement a simple face similarity detector.

1. You must implement a siamese network that accepts two input images
2. The network must output the probability that the two images are the same class
3. Your implementation should use a ResNet architecture

You should turn in the following:

1. A tensorboard screenshot showing that your architecture is, indeed, a siamese architecture
2. Your code
3. A small writeup (<1/2 page) describing your test/training split, your resnet architecture, and the final performance of your classifier.

You should use the Labeled Faces in the Wild-a dataset (also available for download from liftothers).

Your notebook will be graded on the following:

• 35% Correct implementation of Siamese network
• 35% Correct implementation of Resnet
• 20% Reasonable effort to find a good-performing topology
• 10% Results writeup

To help you get started, here's a simple script that will load all of the images and calculate labels. It assumes that the face database has been unpacked in the current directory, and that there exists a file called list.txt that was generated with the following command:

find ./lfw2/ -name \*.jpg > list.txt

After running this code, the data will in the data tensor, and the labels will be in the labels tensor:

from PIL import Image
import numpy as np
 
#
# assumes list.txt is a list of filenames, formatted as
#
# ./lfw2//Aaron_Eckhart/Aaron_Eckhart_0001.jpg
# ./lfw2//Aaron_Guiel/Aaron_Guiel_0001.jpg
# ...
#
 
files = open( './list.txt' ).readlines()
 
data = np.zeros(( len(files), 250, 250 ))
labels = np.zeros(( len(files), 1 ))
 
# a little hash map mapping subjects to IDs
ids = {}
scnt = 0
 
# load in all of our images
ind = 0
for fn in files:
 
    subject = fn.split('/')[3]
    if not ids.has_key( subject ):
        ids[ subject ] = scnt
        scnt += 1
    label = ids[ subject ]
 
    data[ ind, :, : ] = np.array( Image.open( fn.rstrip() ) )
    labels[ ind ] = label
    ind += 1
 
# data is (13233, 250, 250)
# labels is (13233, 1)