Link to contest page on kaggle
Image retrieval is a fundamental problem in computer vision: given a query image, can you find similar images in a large database? This is especially important for query images containing landmarks, which accounts for a large portion of what people like to photograph.
We try to find similar images from a database of images given a query image
If you follow the below instructions it will allow you to install and run the training or testing.
What things you need to install the software and how to install them
- Python 3.6
- Anaconda - Python Environment virtualization so that you dont mess up your system environment
- Keras The best Deep Learning Tool PERIOD ;)
- Tensorflow One of the API used as Backend of Keras
Anaconda is a complete Python distribution embarking automatically the most common packages, and allowing an easy installation of new packages.
Download and install Anaconda from (https://www.continuum.io/downloads). The link for Linux,Mac and Windows are in the website.Following their instruction will install the tool.
- Once Anaconda is installed open anaconda prompt(Windows/PC) Command Line shell(Mac OSX or Unix)
- Run
conda env create -f environment.ymlwill install all packages required for all programs in this repository
-
For Unix like systems
source activate gir -
For PC like systems
activate gir
You can install keras using pip on command line
sudo pip install keras
The environment.yml file for conda is placed in Extra for your ease of installation this has keras
Installing Tensorflow is straight forward using pip on command line
- If CPU then
sudo pip install tensorflow - If GPU then
sudo pip install tensorflow-gpu
The environment.yml file for conda is placed in Extra for your ease of installation this has tensorflow
If you prefer to download the dataset rather than online The code is present in extra/downloadfiles.py
To Run python downloadfiles.py This will download the whole data set including training and testing
In Folders \Train and \Test respectively
- Required Arguments
- arg1: path to index.csv
- arg2: path to hashes.json (this file will be generated by the system)
- arg3: path to test.csv
As of the date this is written (April 27th) we are ranked 59 of 132 teams in the competition.
| S.No | Configuration | Result |
|---|---|---|
| 1 | VGG16 and Kmeans | 0.004 |
- Nihal Soans - nihalsoans91
- Vamsi Nadella - vamsi3309
- Vinay Kumar - vinayawsm
See also the list of contributors who participated in this project.
This project is licensed under the MIT License - see the LICENSE.md file for details
- Hat tip to anyone who's code was used
- Udacity