learnopencv/TSNE

t-SNE for Feature Visualization

This repository contains code for t-SNE for Feature Visualization blog post.

Setup

This code was tested with python 3.7, however, it should work with any python 3.

1. Create and activate virtual environment for experiments with t-SNE.

python3 -m venv venv
source venv/bin/activate

1. install the dependencies

python3 -m pip install -r requirements.txt

Data downloading

Download data from Kaggle and unzip it. The easiest way is to use kaggle console API. To setup it, follow this guide. However, you can download the data using your browser - results will be the same.

After that, execute the following commands:

kaggle datasets download alessiocorrado99/animals10

mkdir -p data

cd data

unzip ../animals10.zip

cd ..

Executing the T-SNE visualization

python3 tsne.py

Additional options:

python3 tsne.py -h

usage: tsne.py [-h] [--path PATH] [--batch BATCH] [--num_images NUM_IMAGES]

optional arguments:
  -h, --help            show this help message and exit
  --path PATH
  --batch BATCH
  --num_images NUM_IMAGES

You can change the data directory with --path argument.

Tweak the --num_images to speed-up the process - by default it is 500, you can make it smaller.

Tweak the --batch to better utilize your PC's resources. The script uses GPU automatically if it available. You may want to increase the batch size to utilize the GPU better or decrease it if the default batch size does not fit your GPU.