Reference implementation of Recursive Cortical Network (RCN) Reference implementation of a two-level RCN model on MNIST classification. See the *Science* article "A generative vision model that trains with high data efficiency and breaks text-based CAPTCHAs" and Vicarious Blog for details. Note: this is an unoptimized reference implementation and is not intended for production. It may work on other system platforms but not guaranteed. You will need the packages listed in to be installed. Clone the repository: The code
is pure Python, so you can run it right away, although you will have to uncompress the ZIP in the data folder manually. Alternatively, install with (setting up a virtual environment beforehand is recommended): Run If you installed via you need to activate the virtual environment: To run a small unit test that trains and tests on 20 MNIST images using one CPU (takes ~2 minutes, accuracy is ~60%): To run a slightly more interesting experiment that trains on 100
images and tests on 20 MNIST images using multiple CPUs (takes <1 min using 7 CPUs, accuracy is ~90%): To test on the full 10k MNIST test set, training on 1000 examples (could take hours depending on the number of available CPUs, average accuracy is ~97. 7+%): Blog post Check out our related blog post. Datasets We used the following datasets for the Science paper: CAPTCHA datasets reCAPTCHA (from google. com) MNIST datasets Original (available at With occlusions (by us
) With noise (by us) MNIST licensing Yann LeCun (Courant Institute, NYU) and Corinna Cortes (Google Labs, New York) hold the copyright of MNIST dataset, which is a derivative work from original NIST datasets. MNIST dataset is made available under the terms of the Creative Commons Attribution-Share Alike 3.
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Reference implementation of Recursive Cortical Network (RCN)
Reference implementation of a two-level RCN model on MNIST classification. See the Science article "A generative vision model that trains with high data efficiency and breaks text-based CAPTCHAs" and Vicarious Blog for details.
Note: this is an unoptimized reference implementation and is not intended for production.
Setup
Note: Python 3.9 is supported. The code was tested on OSX 12.3.1. It may work on other system platforms but not guaranteed. You will need the packages listed in requirements.txt to be installed.
The code is pure Python, so you can run it right away, although you will have to uncompress the ZIP in the data folder manually.
Alternatively, install with (setting up a virtual environment beforehand is recommended):
python setup.py install
Run
If you installed via make you need to activate the virtual environment:
source venv/bin/activate
To run a small unit test that trains and tests on 20 MNIST images using one CPU (takes ~2 minutes, accuracy is ~60%):
python science_rcn/run.py
To run a slightly more interesting experiment that trains on 100 images and tests on 20 MNIST images using multiple CPUs (takes <1 min using 7 CPUs, accuracy is ~90%):
To test on the full 10k MNIST test set, training on 1000 examples (could take hours depending on the number of available CPUs, average accuracy is ~97.7+%):
Yann LeCun (Courant Institute, NYU) and Corinna Cortes (Google Labs, New York) hold the copyright of MNIST dataset, which is a derivative work from original NIST datasets. MNIST dataset is made available under the terms of the Creative Commons Attribution-Share Alike 3.0 license.
