ABrain-One/nn-dataset
Dec 2025 – Mar 2026
3 Months active
Languages Used
Python
Technical Skills
Data AnalysisDeep LearningMachine LearningNeural NetworksPython ProgrammingPython
Aswathy Baiju
PROFILE
Aswathy Baiju
Over three months, contributed to the ABrain-One/nn-dataset repository by building foundational systems for neural network observability and mobile deployment. Developed a comprehensive training metrics tracking system in Python and TensorFlow, capturing loss, accuracy, learning rate, and gradient norms to support reproducible evaluations and data-driven tuning. Enhanced resource monitoring by adding CPU, RAM, and GPU usage reporting, with training summaries persisted for downstream analysis. Delivered a mobile-first age estimation pipeline using a quantized TFLite model, integrating HuggingFace for model management and ensuring secure authentication via environment variables. The work emphasized reliability, reproducibility, and privacy in machine learning workflows and deployment.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
6Total
Bugs
0
Commits
6
Features
3
Lines of code
1,515
Activity Months3
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58 peopleShared Repositories
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Work History
March 2026
1 Commits • 1 Features
Mar 1, 2026March 2026 – ABrain-One/nn-dataset: Delivered a mobile-first age estimation pipeline with a quantized TFLite model, on-device inference, and secure infra. Implemented a TFLite conversion and testing workflow; integrated with HuggingFace for model management; ensured security via environment-variable credentials (no hardcoded tokens). Achieved 9.09 MAE on validation with a 111KB INT8 model based on MobileNetV3-Large. The work enables offline/mobile deployment, improves data privacy, and enhances reproducibility and testing coverage.
1 Commits • 1 Features
Mar 1, 2026March 2026 – ABrain-One/nn-dataset: Delivered a mobile-first age estimation pipeline with a quantized TFLite model, on-device inference, and secure infra. Implemented a TFLite conversion and testing workflow; integrated with HuggingFace for model management; ensured security via environment-variable credentials (no hardcoded tokens). Achieved 9.09 MAE on validation with a 111KB INT8 model based on MobileNetV3-Large. The work enables offline/mobile deployment, improves data privacy, and enhances reproducibility and testing coverage.
March 2026
January 2026
3 Commits • 1 Features
Jan 1, 2026January 2026 monthly performance summary for ABrain-One/nn-dataset focused on strengthening observability and data-driven optimization for ML workloads.
January 2026
3 Commits • 1 Features
Jan 1, 2026January 2026 monthly performance summary for ABrain-One/nn-dataset focused on strengthening observability and data-driven optimization for ML workloads.
December 2025
2 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary for ABrain-One/nn-dataset focusing on feature delivery and observability improvements. Implemented a Neural Network Training Metrics Tracking System to monitor training and validation loss/accuracy per epoch, with additional dynamics captured via learning rate and gradient norms to enable deeper evaluation of training behavior. Established storage and reporting for these metrics to support reproducible evaluations, faster debugging, and data-driven hyperparameter tuning. No major bugs fixed this month; primary work centered on measurement, monitoring, and reliability foundations.
2 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary for ABrain-One/nn-dataset focusing on feature delivery and observability improvements. Implemented a Neural Network Training Metrics Tracking System to monitor training and validation loss/accuracy per epoch, with additional dynamics captured via learning rate and gradient norms to enable deeper evaluation of training behavior. Established storage and reporting for these metrics to support reproducible evaluations, faster debugging, and data-driven hyperparameter tuning. No major bugs fixed this month; primary work centered on measurement, monitoring, and reliability foundations.
December 2025
Activity
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Quality Metrics
Correctness100.0%
Maintainability83.4%
Architecture90.0%
Performance83.4%
AI Usage40.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Data AnalysisDeep LearningHuggingFaceMachine LearningMobile DevelopmentModel DeploymentNeural NetworksPythonPython ProgrammingTensorFlowdata analysisdata processingmachine learning
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