arvindkrishna87/STAT390_SP25_CMIL
May 2025 – May 2025
1 Month active
Languages Used
Jupyter NotebookPython
Technical Skills
Attention MechanismsCNNComputer VisionData AugmentationDeep LearningImage Classification
ArushIyer12
PROFILE
Arushiyer12
Arushi Iyer developed and integrated a Convolutional Block Attention Module (CBAM) into the arvindkrishna87/STAT390_SP25_CMIL repository, enhancing the model’s expressivity and interpretability for image classification tasks. Using Python and PyTorch within Jupyter Notebooks, Arushi engineered a modular approach that preserved compatibility with the existing skeleton code while introducing an optional ResNET pathway. The work included creating visualization assets to demonstrate and evaluate attention mechanisms, supporting rapid experimentation and clear presentation. Through iterative refinement and comprehensive documentation, Arushi ensured the CBAM integration was stable, maintainable, and ready for future evaluation, reflecting a deep understanding of model integration and computer vision.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
19Total
Bugs
0
Commits
19
Features
4
Lines of code
398,864
Activity Months1
Your Network
79 people
Work History
May 2025
19 Commits • 4 Features
May 1, 2025May 2025 performance summary for arvindkrishna87/STAT390_SP25_CMIL. Focused on delivering feature-level CBAM integration into the skeleton code with an optional ResNET pathway, enhancing model expressivity and interpretability while preserving compatibility with existing skeleton workflows. Produced visualization assets for attention to enable quick evaluation and demonstration of the CBAM module. Maintained rigorous iteration across code variants to stabilize integration across the skeleton codebase. Prepared for evaluation and future experimentation with minimal regressions.
19 Commits • 4 Features
May 1, 2025May 2025 performance summary for arvindkrishna87/STAT390_SP25_CMIL. Focused on delivering feature-level CBAM integration into the skeleton code with an optional ResNET pathway, enhancing model expressivity and interpretability while preserving compatibility with existing skeleton workflows. Produced visualization assets for attention to enable quick evaluation and demonstration of the CBAM module. Maintained rigorous iteration across code variants to stabilize integration across the skeleton codebase. Prepared for evaluation and future experimentation with minimal regressions.
May 2025
Activity
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Quality Metrics
Correctness81.0%
Maintainability81.0%
Architecture81.0%
Performance79.0%
AI Usage51.6%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
Attention MechanismsCNNComputer VisionData AugmentationDeep LearningImage ClassificationMachine LearningModel EvaluationModel IntegrationModel TrainingPyTorch
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