huggingface/transformers
Dec 2025 – Dec 2025
1 Month active
Languages Used
Python
Technical Skills
Computer VisionDeep LearningMachine LearningModel DevelopmentPyTorch
Lihe Yang
PROFILE
Lihe Yang
Lihe Yang developed the Pixio Vision Foundation Model, a ViT-based architecture for depth estimation and image classification, within the huggingface/transformers repository. Leveraging Python and PyTorch, Lihe modularized the model components to enhance maintainability and scalability, and integrated pre-trained Pixio models to accelerate downstream deployment. The work included comprehensive documentation and robust test coverage, ensuring code quality and facilitating adoption by other teams. Lihe collaborated with co-authors to align the implementation with core review standards, providing a scalable foundation for depth-enabled computer vision tasks. This contribution addressed both technical robustness and usability for future machine learning projects.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
1,739
Activity Months1
Your Network
252 peopleWork History
December 2025
1 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary: Delivered the Pixio Vision Foundation Model (ViT-based) with depth estimation and image classification in huggingface/transformers. Added pre-trained Pixio models, extensive documentation, and tests; modularized components to improve maintainability and scalability. This work provides a scalable foundation for depth-enabled vision tasks and accelerates downstream deployment while ensuring quality through tests and documentation. Collaborative work included co-authored contributions with Pablo Montalvo.
1 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary: Delivered the Pixio Vision Foundation Model (ViT-based) with depth estimation and image classification in huggingface/transformers. Added pre-trained Pixio models, extensive documentation, and tests; modularized components to improve maintainability and scalability. This work provides a scalable foundation for depth-enabled vision tasks and accelerates downstream deployment while ensuring quality through tests and documentation. Collaborative work included co-authored contributions with Pablo Montalvo.
December 2025
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage60.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Computer VisionDeep LearningMachine LearningModel DevelopmentPyTorch
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