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
Developed and integrated the Charades-STA dataset into the lmms-eval repository to enable temporal grounding tasks for video understanding. Leveraged Python and YAML to build configuration scaffolding, evaluation scripts, and utility functions that process and assess video-based temporal events described in text. This work expanded the framework’s evaluation capabilities, allowing models to identify precise time intervals for events within videos. The integration supports more realistic benchmarking and streamlines future dataset onboarding. Focused on dataset integration, evaluation metrics, and machine learning, the contribution enhanced model assessment workflows without introducing bugs, reflecting a methodical and robust engineering approach within the project.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
380
Activity Months1
Your Network
92 peopleSame Organization
@ntu.edu.sg3
Shared Repositories
89
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
Repositories Contributed To
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