landing-ai/vision-agent
Nov 2024 – Dec 2024
2 Months active
Languages Used
NumPyOpenCVPython
Technical Skills
API IntegrationBackend DevelopmentBug FixingComputer VisionImage ProcessingMachine Learning
Hugo Honda
PROFILE
Hugo Honda
Hugo Honda developed advanced document understanding and vision capabilities for the landing-ai/vision-agent repository over a two-month period. He built a Document Understanding Toolkit that extracts structured information such as text, tables, and images from diverse document layouts and enables question answering on document images by normalizing extracted data. Hugo also integrated the SigLip classification model, replacing CLIP, and improved the Flux image inpainting pipeline by enforcing dimension constraints and enhancing error handling. His work leveraged Python, OpenCV, and NumPy, focusing on robust API integration, backend development, and testing to deliver more reliable, user-facing workflows for automated vision and document analysis.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
6Total
Bugs
1
Commits
6
Features
2
Lines of code
306
Activity Months2
Your Network
18 peopleSame Organization
@landing.ai4
Work History
December 2024
2 Commits • 1 Features
Dec 1, 2024December 2024 saw the delivery of the Document Understanding Toolkit for Vision Agent, introducing two user-facing capabilities: (1) Document Analysis to extract structured information (text, tables, pictures, charts) from diverse document layouts and (2) Document QA to answer questions based on document images with normalization of extracted data prior to response. This work enhances automated document understanding within Vision Agent and enables faster, more informed decision-making.
2 Commits • 1 Features
Dec 1, 2024December 2024 saw the delivery of the Document Understanding Toolkit for Vision Agent, introducing two user-facing capabilities: (1) Document Analysis to extract structured information (text, tables, pictures, charts) from diverse document layouts and (2) Document QA to answer questions based on document images with normalization of extracted data prior to response. This work enhances automated document understanding within Vision Agent and enables faster, more informed decision-making.
December 2024
November 2024
4 Commits • 1 Features
Nov 1, 2024November 2024: Delivered key capabilities in Vision Agent with SigLip integration and improved Flux image inpainting robustness. Implemented the SigLip classification tool, added the siglip_classification function, and built integration tests, including updates to function/endpoint names for consistency. Fixed Flux image resizing and error handling to meet model constraints (multiples of 8, max 512x512 for large images) and strengthened validation for invalid dimensions and mask values. Result is a more capable, reliable vision pipeline with clearer APIs and reduced failure modes.
November 2024
4 Commits • 1 Features
Nov 1, 2024November 2024: Delivered key capabilities in Vision Agent with SigLip integration and improved Flux image inpainting robustness. Implemented the SigLip classification tool, added the siglip_classification function, and built integration tests, including updates to function/endpoint names for consistency. Fixed Flux image resizing and error handling to meet model constraints (multiples of 8, max 512x512 for large images) and strengthened validation for invalid dimensions and mask values. Result is a more capable, reliable vision pipeline with clearer APIs and reduced failure modes.
Activity
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Quality Metrics
Correctness86.8%
Maintainability83.4%
Architecture83.4%
Performance70.0%
AI Usage30.0%
Skills & Technologies
Programming Languages
NumPyOpenCVPython
Technical Skills
API IntegrationBackend DevelopmentBug FixingComputer VisionImage ProcessingLLM IntegrationMachine LearningNatural Language ProcessingPython DevelopmentTestingTool DevelopmentTool Integration
Repositories Contributed To
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