EvolvingLMMs-Lab/lmms-eval
Feb 2025 – Feb 2025
1 Month active
Languages Used
PythonYAML
Technical Skills
Dataset IntegrationEvaluation MetricsMachine LearningTemporal GroundingVideo Understanding
Yuanhan Zhang
PROFILE
Yuanhan Zhang
Yuanhan Zhang integrated the Charades-STA dataset into the EvolvingLMMs-Lab/lmms-eval repository, enabling temporal grounding tasks for video understanding research. Using Python and YAML, Yuanhan developed configuration scaffolding, evaluation scripts, and utility functions to process and assess video-based temporal events described in text. This work allowed models to identify precise time intervals for events within videos, expanding the framework’s evaluation capabilities and supporting more realistic benchmarking. The integration streamlined dataset onboarding and laid the groundwork for future video-language research. Yuanhan’s contributions demonstrated depth in dataset integration, evaluation metrics, and machine learning, with a focus on robust, end-to-end engineering solutions.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
380
Activity Months1
Your Network
77 peopleSame Organization
@ntu.edu.sg1
Work History
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025: Delivered Charades-STA dataset integration for temporal grounding in the lmms-eval framework. Implemented dataset integration with configuration scaffolding, evaluation scripts, and utility functions to process and evaluate video-based temporal events described in text, enabling models to identify precise time intervals. This expands the evaluation surface, supports more realistic benchmarking, and paves the way for future dataset integrations and video-language research. No notable bugs reported this month. Major impact includes enhanced model assessment capabilities and faster onboarding for new datasets.
1 Commits • 1 Features
Feb 1, 2025February 2025: Delivered Charades-STA dataset integration for temporal grounding in the lmms-eval framework. Implemented dataset integration with configuration scaffolding, evaluation scripts, and utility functions to process and evaluate video-based temporal events described in text, enabling models to identify precise time intervals. This expands the evaluation surface, supports more realistic benchmarking, and paves the way for future dataset integrations and video-language research. No notable bugs reported this month. Major impact includes enhanced model assessment capabilities and faster onboarding for new datasets.
February 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture90.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
PythonYAML
Technical Skills
Dataset IntegrationEvaluation MetricsMachine LearningTemporal GroundingVideo Understanding
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