DataBytes-Organisation/Project-Echo
Aug 2025 – Aug 2025
1 Month active
Languages Used
Jupyter NotebookPythonYAML
Technical Skills
Audio ProcessingCIFAR10 DatasetCloud StorageComputer VisionConfiguration ManagementData Augmentation
newbee1905
PROFILE
Newbee1905
Developed a scalable, deployment-ready audio model training platform within the DataBytes-Organisation/Project-Echo repository, focusing on PyTorch integration and Hydra-driven configuration. The work included building a configurable audio training pipeline with spectrogram augmentation, dataset loading, and a robust train-eval loop. Expanded model support by integrating PANNS and MobileNetV2 with ArcFace, and introduced Quantization Aware Training for EfficientNetV2. Enhanced the quantization workflow and training utilities, improving performance and stability. Addressed critical bugs related to training loss calculation, autocast device handling, and Google Cloud notebook warnings. Utilized Python, Jupyter Notebook, and YAML, demonstrating expertise in deep learning and configuration management.
Overall Statistics
Feature vs Bugs
63%Features
Repository Contributions
20Total
Bugs
3
Commits
20
Features
5
Lines of code
7,932
Activity Months1
Your Network
47 peopleShared Repositories
47
Adrian FariaMember
Akshat GuptaMember
AshbinMember
atulrane03Member
first startMember
Brayden de KoningMember
Bhanu Singh ThakurMember
fordgt91011Member
427ElijahMember
Work History
August 2025
20 Commits • 5 Features
Aug 1, 2025Monthly summary for 2025-08 focusing on key accomplishments, major bug fixes, impact and skills demonstrated. Highlights include the Torch groundwork and audio training pipeline using Hydra for configurable YAML-driven experiments, expansion of PANNS and MobileNetV2 with ArcFace integration, QAT support for EfficientNetV2, quantization workflow improvements, and training utilities with stability improvements. Addressed several bugs (train_loss override, autocast device type typo, and Google Cloud notebook warnings). Result: a scalable, deployment-ready training platform for audio models with improved performance, efficiency, and developer productivity.
20 Commits • 5 Features
Aug 1, 2025Monthly summary for 2025-08 focusing on key accomplishments, major bug fixes, impact and skills demonstrated. Highlights include the Torch groundwork and audio training pipeline using Hydra for configurable YAML-driven experiments, expansion of PANNS and MobileNetV2 with ArcFace integration, QAT support for EfficientNetV2, quantization workflow improvements, and training utilities with stability improvements. Addressed several bugs (train_loss override, autocast device type typo, and Google Cloud notebook warnings). Result: a scalable, deployment-ready training platform for audio models with improved performance, efficiency, and developer productivity.
August 2025
Activity
Loading activity data...
Quality Metrics
Correctness81.6%
Maintainability83.0%
Architecture83.6%
Performance77.0%
AI Usage23.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPythonYAML
Technical Skills
Audio ProcessingCIFAR10 DatasetCloud StorageComputer VisionConfiguration ManagementData AugmentationData VisualizationDebuggingDeep LearningDependency ManagementEnvironment SetupGoogle Cloud PlatformJupyter NotebookKnowledge DistillationMachine Learning
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline