| # 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. | |
| Clone the repository: | |
| ``` | |
| git clone https://github.com/vicariousinc/science_rcn.git | |
| ``` | |
| 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%): | |
| ``` | |
| python science_rcn/run.py --train_size 100 --test_size 20 --parallel | |
| ``` | |
| 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+%): | |
| ``` | |
| python science_rcn/run.py --full_test_set --train_size 1000 --parallel --pool_shape 25 --perturb_factor 2.0 | |
| ``` | |
| ## Blog post | |
| Check out our related blog post. | |
| ## Datasets | |
| We used the following datasets for the Science paper: | |
| CAPTCHA datasets | |
| - reCAPTCHA (from google.com) | |
| - BotDetect (from captcha.com) | |
| - Paypal (from paypal.com) | |
| - Yahoo (from yahoo.com) | |
| MNIST datasets | |
| - Original (available at http://yann.lecun.com/exdb/mnist/) | |
| - 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 3.0 license. | |