The test/
directory includes 500 test images we ran our classification model over to achieve
a mean average precision of 0.9276 (in the ballpark of the top 200 Kaggle classifiers). Our
predictions are available in test/final.csv
. These results are also available in a
Google Sheet
for members of the Columbia community.
# Requires python3, pip3
pip3 install -r requirements.txt
./scripts/fetch_grayscale_images.py
./scripts/generate_automl_vision_csv.py <class> <count> > output.csv
The ndjson test images from Kaggle's competition have been transformed into 28x28
grayscale images matching the training ones available from Google Cloud Console.
They are available in the ./data/images.tar.gz
archive.
npm install
// Run all images in the ./images folder through the 340 binary classifiers
node ./scripts/predict.js
// Run all images in the ./images folder through a specific classifier
node ./scripts/predict2.js <automl model id> <output filename>
The included IPython notebooks in ipynb/
contain routines for training GANs and applying image-based transformations to rasterized, 28x28 grayscale images from the quickdraw dataset.