cs501r_f2016:tmp
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| - | ====Objective:==== | ||
| - | To gain experience coding a DNN architecture and learning program end-to-end, and to gain experience with Siamese network and ResNets. | ||
| - | |||
| - | ---- | ||
| - | ====Deliverable:==== | ||
| - | |||
| - | For this lab, you will need to implement a simple face similarity detector. | ||
| - | |||
| - | - You must implement a siamese network that accepts two input images | ||
| - | - The network must output the probability that the two images are the same class | ||
| - | - Your implementation should use a ResNet architecture | ||
| - | |||
| - | You should turn in the following: | ||
| - | |||
| - | - A tensorboard screenshot showing that your architecture is, indeed, a siamese architecture | ||
| - | - Your code | ||
| - | - 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 [[http://www.openu.ac.il/home/hassner/data/lfwa/|Labeled Faces in the Wild-a]] dataset (also available for | ||
| - | [[http://liftothers.org/byu/lfwa.tar.gz|download from liftothers]]). | ||
| - | |||
| - | ---- | ||
| - | ====Grading standards:==== | ||
| - | |||
| - | 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 | ||
| - | |||
| - | ---- | ||
| - | ====Description:==== | ||
| - | |||
| - | ---- | ||
| - | ====Hints:==== | ||
| - | |||
| - | 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: | ||
| - | |||
| - | <code bash> | ||
| - | find ./lfw2/ -name \*.jpg > list.txt | ||
| - | </code> | ||
| - | |||
| - | After running this code, the data will in the ''data'' tensor, and the labels will be in the ''labels'' tensor: | ||
| - | |||
| - | <code python> | ||
| - | |||
| - | 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) | ||
| - | |||
| - | </code> | ||
cs501r_f2016/tmp.txt ยท Last modified: 2021/06/30 23:42 (external edit)
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