DeepProduct

To get started, make sure you're using Python 3 and then create an environment using something like

virtualenv env
source env/bin/activate
pip install -r requirements.txt

this will install Tensorflow and Keras (amongst other things), so it may take slightly longer than usual.

Link to the image data in the dropbox:

ln -s ~/Dropbox\ \(Datascope\ Analytics\)/DeepProduct/Data/ Data

With the environment active, view the notebook by launching the jupyter server

jupyter notebook

The code will need to download several files of order ~100MB when being first run, but will cache them for future execution.

For those working on developing the tentatively named couch_tinder application, you should also create a symlink within the static directory. Please call it to_dropbox, otherwise your web app will be Brokeymon.

cd static
ln -s ~/Dropbox\ \(Datascope\ Analytics\)/DeepProduct/Data/ to_dropbox

Name		Name	Last commit message	Last commit date
Latest commit History 185 Commits
.ipynb_checkpoints		.ipynb_checkpoints
blog_images		blog_images
images		images
output		output
static		static
templates		templates
.DS_Store		.DS_Store
.gitignore		.gitignore
Attribute Subset generation.ipynb		Attribute Subset generation.ipynb
Category label re-balance.ipynb		Category label re-balance.ipynb
DF feature gen and test.ipynb		DF feature gen and test.ipynb
DF t-sne and pairs.ipynb		DF t-sne and pairs.ipynb
DF_5000_FC6_full_tsne.png		DF_5000_FC6_full_tsne.png
DF_5000_FC6_full_tsne2.png		DF_5000_FC6_full_tsne2.png
DF_retrain.py		DF_retrain.py
Deep Fashion Network Re-train Category.ipynb		Deep Fashion Network Re-train Category.ipynb
Deep Fashion Network Re-train.ipynb		Deep Fashion Network Re-train.ipynb
Deep Fashion feature evaluation.ipynb		Deep Fashion feature evaluation.ipynb
Deep Fashion.ipynb		Deep Fashion.ipynb
Dress interpolation.ipynb		Dress interpolation.ipynb
Exploring features.ipynb		Exploring features.ipynb
LSH.ipynb		LSH.ipynb
MIH Profiling.ipynb		MIH Profiling.ipynb
MIH.ipynb		MIH.ipynb
MakeDopeSubset.py		MakeDopeSubset.py
MultiIndexHash.py		MultiIndexHash.py
NatBlogDraft.MD		NatBlogDraft.MD
README.md		README.md
Untitled.ipynb		Untitled.ipynb
Untitled1.ipynb		Untitled1.ipynb
VGG Exploration.ipynb		VGG Exploration.ipynb
Visual Search Demo.ipynb		Visual Search Demo.ipynb
all_pairs.txt		all_pairs.txt
blog_content_ideas.md		blog_content_ideas.md
dope_nope_schema.sql		dope_nope_schema.sql
dope_or_nope.py		dope_or_nope.py
fc6_base_H.txt		fc6_base_H.txt
fc6_full_LSH.txt		fc6_full_LSH.txt
fc6_full_MIH.txt		fc6_full_MIH.txt
fc6_full_trained_H.txt		fc6_full_trained_H.txt
fc6_full_trained_L2.txt		fc6_full_trained_L2.txt
fc6_subset_trained_L2.txt		fc6_subset_trained_L2.txt
fc7_base_H.txt		fc7_base_H.txt
feature interpolation.ipynb		feature interpolation.ipynb
make df subset.ipynb		make df subset.ipynb
multi process batch.ipynb		multi process batch.ipynb
requirements.txt		requirements.txt
write_bbox_copies.py		write_bbox_copies.py
wsgi.py		wsgi.py

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