cs501r_f2016:tmp
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| ====Description:==== | ====Description:==== | ||
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| + | For this lab, you should implement a Siamese network, and train it to recognize whether or not two faces are the same or different. | ||
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| + | No scaffolding code (except for a simple script for loading the images below) will be provided. The goal of this lab is for you to experiment with implementing an idea end-to-end. | ||
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| + | The steps for completion of this lab are: | ||
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| + | - Load all of the data. Create a test/training split. | ||
| + | - Establish a baseline accuracy (ie, if you randomly predict same/different, what accuracy do you achieve?) | ||
| + | - Use tensorflow to create your siamese network. | ||
| + | - Use ResNets to extract features from the images | ||
| + | - Make sure that parameters are shared across both halves of the network! | ||
| + | - Train the network using an optimizer of your choice | ||
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| + | Note: you will NOT be graded on the accuracy of your final classifier, as long as you make a good faith effort to come up with something that performs reasonably well. | ||
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| + | Your ResNet should extract a vector of features from each image. Those feature vectors should then be compared to calculate an "energy"; that energy should then be input into a contrastive loss function, as discussed in class. | ||
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| + | Note that some people in the database only have one image. These images are still useful, however (why?), so don't just throw them away. | ||
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| + | ---- | ||
| + | ====Writeup:==== | ||
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| + | As discussed in the "Deliverable" section, your writeup must include the following: | ||
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| + | - A description of your test/training split | ||
| + | - A description of your resnet architecture (layers, strides, nonlinearities, etc.) | ||
| + | - How you assessed whether or not your architecture was working | ||
| + | - The final performance of your classifier | ||
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| + | This writeup should be small - between 1/2 - 1 page. You don't need to wax eloquent. | ||
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cs501r_f2016/tmp.1478715707.txt.gz · Last modified: 2021/06/30 23:40 (external edit)
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