ncalab 0.1.0

Training code and pre-trained models for Neural Cellular Automata on different datasets and tasks

NCALab

NCALab makes it easy to create Neural Cellular Automata (NCA) implementations for various downstream tasks, such as image segmentation, classification and generation.

Features

Features of NCALab include:

• Easy training and evaluation of NCA models
• Cascaded multi-scale training
• Tensorboard integration with default presets
• Training with k-fold cross-validation
• Convenience features: Fixing random seeds, selecting compute devices, data processing

Getting started

Perhaps the best way of getting started with NCALab is to take a look at the provided usage example tasks, starting with the Growing Emoji task.

Usage Example Tasks

So far, the following example tasks are implemented in NCALab:

• Image Generation:
  • Growing NCA for emoji generation
    • Training and evaluation
    • Fine-tuning of a pre-trained emoji generator
    • Hyperparameter search
• Image Classification:
  • Self-classifying MNIST digits
  • MedMNIST image classification (PathMNIST, BloodMNIST)
• Image Segmentation:
  • Endoscopic polyp segmentation (Kvasir-SEG, public)
  • Capsule endoscopic bleeding segmentation (KID2 dataset, proprietary)
• Monocular Depth Estimation
  • Capsule endoscopic monocular depth estimation

You can find those example tasks inside the tasks/ directory and its subfolders.

A good starting point to get started with NCAs is the famous Growing Lizard emoji example.

python3 tasks/growing_emoji/train_growing_emoji.py

Run this script to generate a GIF of the trained model's prediction:

python3 tasks/growing_emoji/eval_growing_emoji.py

Installation

Run

pip install git+https://github.com/MECLabTUDA/NCAlab

to install the most recent version of NCALab. We recommend to install NCALab in a virtual environment.

Tensorboard integration

We recommend your training progress in Tensorboard. To launch tensorboard, run

tensorboard --logdir=runs

in a separate terminal. Once it is running, it should show you the URL the tensorboard server is running on, which is localhost:6006 by default. Alternatively, you may use the tensorboard integration of your IDE.