axinc-ai/ailia-models
Jan 2025 – Mar 2025
3 Months active
Languages Used
PythonShell
Technical Skills
Computer VisionDeep LearningModel IntegrationPython DevelopmentShell ScriptingMachine Learning
Koichi73
PROFILE
Koichi73
Over a three-month period, contributed to the axinc-ai/ailia-models repository by delivering two features and a bug fix focused on computer vision and natural language processing. Integrated the Gazelle gaze estimation model, enabling end-to-end workflows for analyzing gaze behavior in images and videos, with supporting Python scripts, face-detection utilities, and visualization tools. Developed a zero-shot Japanese text classification feature using multilingual models and a command-line interface, streamlining deployment for Japanese-language tasks. Addressed a robustness issue in candidate label parsing, ensuring reliable configuration via CLI arguments. Work emphasized Python development, model deployment, and integration, supporting reproducible and accessible machine learning solutions.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
3Total
Bugs
1
Commits
3
Features
2
Lines of code
733
Activity Months3
Your Network
14 peopleShared Repositories
14
Work History
March 2025
1 Commits
Mar 1, 2025Summary for 2025-03: Implemented a robustness fix for Candidate Label Parsing in axinc-ai/ailia-models, ensuring the model respects labels provided via command-line arguments instead of a hardcoded constant. The change improves accuracy, reproducibility, and deployment reliability by guaranteeing the intended label set is used across runs. Overall, this work reduces mislabeling risk, strengthens configuration management, and supports more reliable experimentation.
1 Commits
Mar 1, 2025Summary for 2025-03: Implemented a robustness fix for Candidate Label Parsing in axinc-ai/ailia-models, ensuring the model respects labels provided via command-line arguments instead of a hardcoded constant. The change improves accuracy, reproducibility, and deployment reliability by guaranteeing the intended label set is used across runs. Overall, this work reduces mislabeling risk, strengthens configuration management, and supports more reliable experimentation.
March 2025
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for axinc-ai/ailia-models: Implemented Zero-shot Japanese Classification feature with a new model and a CLI-based usage workflow enabling classification of Japanese text without labeled data. Delivered with a focused commit and CLI tooling to facilitate fast adoption in production environments. The release strengthens multilingual NLP capabilities and accelerates time-to-value for Japanese-language tasks.
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for axinc-ai/ailia-models: Implemented Zero-shot Japanese Classification feature with a new model and a CLI-based usage workflow enabling classification of Japanese text without labeled data. Delivered with a focused commit and CLI tooling to facilitate fast adoption in production environments. The release strengthens multilingual NLP capabilities and accelerates time-to-value for Japanese-language tasks.
January 2025
1 Commits • 1 Features
Jan 1, 2025Month: 2025-01. This period delivered the Gazelle gaze estimation model integration into the axinc-ai/ailia-models repository, enabling end-to-end gaze estimation workflows for images and videos. The feature package includes Python scripts to run on images and videos, face-detection utilities, visualization components to interpret results, and updated documentation. A dedicated script to download Gazelle models was added to streamline onboarding and deployment. Overall, this adds a new product analytics and UX research capability by analyzing gaze behavior, with an accessible deployment path for engineers and data scientists. No major bugs reported this month.
1 Commits • 1 Features
Jan 1, 2025Month: 2025-01. This period delivered the Gazelle gaze estimation model integration into the axinc-ai/ailia-models repository, enabling end-to-end gaze estimation workflows for images and videos. The feature package includes Python scripts to run on images and videos, face-detection utilities, visualization components to interpret results, and updated documentation. A dedicated script to download Gazelle models was added to streamline onboarding and deployment. Overall, this adds a new product analytics and UX research capability by analyzing gaze behavior, with an accessible deployment path for engineers and data scientists. No major bugs reported this month.
January 2025
Activity
Loading activity data...
Quality Metrics
Correctness83.4%
Maintainability80.0%
Architecture76.6%
Performance86.6%
AI Usage46.6%
Skills & Technologies
Programming Languages
PythonShell
Technical Skills
Computer VisionDeep LearningMachine LearningModel DeploymentModel IntegrationNatural Language ProcessingPython DevelopmentShell Scripting
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline