MonashDeepNeuron/Neural-Cellular-Automata
Nov 2024 – Feb 2025
3 Months active
Languages Used
Jupyter NotebookPython
Technical Skills
Data VisualizationDeep LearningGPU ComputingImage ProcessingMachine LearningMatplotlib
CaMo111
PROFILE
Camo111
Worked on the MonashDeepNeuron/Neural-Cellular-Automata repository, delivering foundational features for neural cellular automata and image segmentation workflows. Developed pool-based training with image persistence and interactive grid visualization, leveraging Python, PyTorch, and Matplotlib to improve experiment reproducibility and model introspection. Established an end-to-end data pipeline and environment scaffolding to accelerate prototyping and support reproducible research. Implemented basic image segmentation algorithms and laid the groundwork for the Med-NCA model, including model definition and training script scaffolding. Maintained repository hygiene by removing redundant files and improving onboarding readiness, ensuring a streamlined codebase for future development and collaborative experimentation.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
11Total
Bugs
0
Commits
11
Features
5
Lines of code
1,938
Activity Months3
Your Network
8 peopleShared Repositories
8
Work History
February 2025
1 Commits • 1 Features
Feb 1, 2025Month: 2025-02 — Focused repository hygiene improvements for MonashDeepNeuron/Neural-Cellular-Automata. Completed cleanup tasks to reduce clutter, streamline maintenance, and improve onboarding with a targeted codebase cleanup of the dev branch.
1 Commits • 1 Features
Feb 1, 2025Month: 2025-02 — Focused repository hygiene improvements for MonashDeepNeuron/Neural-Cellular-Automata. Completed cleanup tasks to reduce clutter, streamline maintenance, and improve onboarding with a targeted codebase cleanup of the dev branch.
February 2025
December 2024
2 Commits • 2 Features
Dec 1, 2024December 2024 monthly summary for MonashDeepNeuron/Neural-Cellular-Automata. Delivered foundational capabilities for image segmentation and Med-NCA model development, establishing an end-to-end data pipeline, environment setup, and sample data to enable rapid experimentation and reproducibility. These foundations position the project for upcoming production-grade pipelines and continued model development in 2025.
December 2024
2 Commits • 2 Features
Dec 1, 2024December 2024 monthly summary for MonashDeepNeuron/Neural-Cellular-Automata. Delivered foundational capabilities for image segmentation and Med-NCA model development, establishing an end-to-end data pipeline, environment setup, and sample data to enable rapid experimentation and reproducibility. These foundations position the project for upcoming production-grade pipelines and continued model development in 2025.
November 2024
8 Commits • 2 Features
Nov 1, 2024November 2024 summary for MonashDeepNeuron/Neural-Cellular-Automata: Delivered pool-based training with image persistence and an interactive Grid visualization for the GCA model. Fixed epoch-related bugs in the new loss system and stabilized pooling training to reduce instability. Improved experiment reproducibility, training reliability, and visibility into model dynamics, enabling faster experimentation and clearer business value. Technologies demonstrated include Python ML pipelines, pooling/sample-pool management, LR scheduling, image persistence tooling, and Matplotlib-based interactive visualization.
8 Commits • 2 Features
Nov 1, 2024November 2024 summary for MonashDeepNeuron/Neural-Cellular-Automata: Delivered pool-based training with image persistence and an interactive Grid visualization for the GCA model. Fixed epoch-related bugs in the new loss system and stabilized pooling training to reduce instability. Improved experiment reproducibility, training reliability, and visibility into model dynamics, enabling faster experimentation and clearer business value. Technologies demonstrated include Python ML pipelines, pooling/sample-pool management, LR scheduling, image persistence tooling, and Matplotlib-based interactive visualization.
November 2024
Activity
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Quality Metrics
Correctness78.2%
Maintainability80.0%
Architecture76.2%
Performance65.4%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
Computer VisionData AugmentationData VisualizationDeep LearningGPU ComputingImage ProcessingImage SegmentationMachine LearningMatplotlibModel ImplementationModel TrainingObject-Oriented ProgrammingPyTorchPythonVideo Processing
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