Create README.md

added readme

Author: vinayakarannil

README.md

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+# Image similarity search using deep learning
+This project demonstrates, how we can make use of deep learning to do state-of-the-art image similarity search. I have used tensorflow and some publicly available datasets.
+
+## Results
+![alt text](https://cdn-images-1.medium.com/max/800/1*dd0QDhLZAjBKD5JZgJCI9w.png)
+![alt text](https://cdn-images-1.medium.com/max/800/0*ziXWbDIrdW_qJMCL.png)
+![alt text](https://cdn-images-1.medium.com/max/800/0*gL9UZQFdwFk-9smY.png)
+
+## How to run
+1. Download [imagenet](https://drive.google.com/open?id=1UOyZ8166qM3SzxGvaUeWpBzSUfoQLUjJ) folder, extraxt and keep it in server directory
+2. Download datasets for [footwares](http://vision.cs.utexas.edu/projects/finegrained/utzap50k/), [apparels](http://mmlab.ie.cuhk.edu.hk/projects/DeepFashion/InShopRetrieval.html) keep them inside a directory under upload folder. Final folder strcture will be as below
+```
+      root folder  
+      │
+      └───lib
+      │   
+      └───server
+      |   │───rest-server.py
+      |   │───imagenet
+      |   │───static
+      |   │───templates
+      |   │───uploads
+      |        │────dogs_and_cats
+      |              │────shoes
+      |              │────sandals
+      |              │────slippers
+      |              │────boots
+      |              │────apparels
+```
+3. Run image vectorizer which passes each data through an inception-v3 model and collects the bottleneck layer vectors and stores in disc. Edit dataset paths accordingly indide the image_vectorizer.py
+```
+  python server/image_vectorizer.py 
+ ```
+   This will generate two files namely, image_list.pickle and saved_features.txt. Keep them inside lib folder where search.py script is available.
+   
+4. Start the server by running rest-server.py. This project uses flask based REST implementation for UI
+```
+  python server/rest-server.py 
+```
+5. Once the server starts up, access the url 127.0.0.1:5000 to get the UI. Now upload any file and see 9 similar images. You can change the value of K from 9 to any values, but dont foreget to update the html file accordingly for displaying.
+
+One interesting application of this project is a recommendation engine based on image features.Here is an example of similar project of mine. Here instead of a web UI i have used an android UI. Once the user clicks a product image, the image will go to the server and k-number of similar product images can be displayed on UI as product recommendations. Theses rescommendations are purely based on image similarity. This kind of recommendations have high potentials in fashion-based ecommerce industry.
+
+![Example Results](server/static/images/result1.jpg)
+![Example Results](server/static/images/result2.jpg)
+