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
Developed and deployed ONNX Runtime inference support for GFPGAN within 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 robust error handling to prevent runtime issues during face detection. By introducing a safety guard that blocks unsupported operations when ONNX is enabled, the developer improved deployment flexibility and production stability. This contribution demonstrated skills in computer vision, model deployment, and defensive programming, addressing both feature expansion and reliability concerns.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
3Total
Bugs
1
Commits
3
Features
1
Lines of code
25
Activity Months1
Your Network
17 peopleSame Organization
@axell.co.jp3
Shared Repositories
14
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
Repositories Contributed To
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