axinc-ai/ailia-models
Nov 2024 – Nov 2024
1 Month active
Languages Used
Python
Technical Skills
Computer VisionError HandlingImage ProcessingInference OptimizationMachine LearningModel Deployment
k-hayashi
PROFILE
K-hayashi
K. Hayashi developed ONNX Runtime inference capability for GFPGAN in the axinc-ai/ailia-models repository, enabling users to select between ailia Net and ONNX Runtime via a new feature flag. The work involved integrating ONNX Runtime with Python, optimizing inference by correcting numeric precision from double to float32, and implementing defensive programming to prevent face detection errors when ONNX is enabled. By addressing both feature expansion and error handling, Hayashi improved deployment flexibility and runtime stability. The project demonstrated skills in computer vision, model deployment, and inference optimization, reflecting a focused and practical approach to cross-framework machine learning engineering.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
3Total
Bugs
1
Commits
3
Features
1
Lines of code
25
Activity Months1
Your Network
16 peopleSame Organization
@axell.co.jp3
Work History
November 2024
3 Commits • 1 Features
Nov 1, 2024Month 2024-11: Delivered GFPGAN ONNX Runtime inference capability and hardened ONNX usage to support broader deployment scenarios. Key outcomes include introducing a --onnx flag to select between the ailia Net and ONNX Runtime for GFPGAN inference, correcting precision handling in the ONNX path from double to float32, and implementing a safety guard that asserts when ONNX is enabled to prevent face_det runtime errors. These changes expand deployment options, improve stability, and reduce risk in production. Technologies/skills demonstrated include ONNX Runtime integration, feature flag design (--onnx), numeric precision management, and defensive programming for safer cross-framework operation.
3 Commits • 1 Features
Nov 1, 2024Month 2024-11: Delivered GFPGAN ONNX Runtime inference capability and hardened ONNX usage to support broader deployment scenarios. Key outcomes include introducing a --onnx flag to select between the ailia Net and ONNX Runtime for GFPGAN inference, correcting precision handling in the ONNX path from double to float32, and implementing a safety guard that asserts when ONNX is enabled to prevent face_det runtime errors. These changes expand deployment options, improve stability, and reduce risk in production. Technologies/skills demonstrated include ONNX Runtime integration, feature flag design (--onnx), numeric precision management, and defensive programming for safer cross-framework operation.
November 2024
Activity
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Quality Metrics
Correctness80.0%
Maintainability86.6%
Architecture73.4%
Performance66.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Computer VisionError HandlingImage ProcessingInference OptimizationMachine LearningModel DeploymentONNX Runtime
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