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Running the baseline code of the ISC2021 challenge

This is a step-by-step walkthrough on how to get a valid submission for the ISC2021 challenge.

This tutorial will guide you to get baseline results for the ISC2021 competion, how to evaluate them and get a submittable file.

Downloading the data

Get the images for query, reference and training sets as described in the Driven Data page

https://www.drivendata.org/competitions/80/competition-image-similarity-2-dev/data/

Please be patient, this is a total of 350 GB of data. Note that the training images are not required for the first steps of the process.

In the following, we assume that the images are available in the images/queries, images/references and images/train subdirectories.

While the data is downloading, you can install the required packages and compile some code.

Cloning & installing dependencies

First, clone this repo:

git clone [email protected]:facebookresearch/isc2021.git

Follow the steps below to install all the required dependencies in order to run the ISC evaluation code. Note: The code in this repo requires 3.5 <= Python <= 3.8.

conda create -n isc2021 python=3.8 -y && conda activate isc2021
pip install -e isc2021/
conda install -c pytorch faiss-gpu

Steps

The tutorial breaks down in steps from easiest and fastest to more complicated.

Step 1: GIST descriptors on a small subset

Step 2: GIST descriptors with PCA

Step 3: GIST descriptors on the full dataset

Step 4: Multigrain descriptors on the small subset

Step 5: Multigrain descriptors on the full dataset