hao-ai-lab/FastVideo
Dec 2025 – Dec 2025
1 Month active
Languages Used
Python
Technical Skills
Deep LearningMachine LearningPython ProgrammingPython scriptingVideo Processingdata analysis
Ketaki Tank
PROFILE
Ketaki Tank
During December 2025, Ktank developed a robust video quality benchmarking module for the hao-ai-lab/FastVideo repository, focusing on implementing Fréchet Video Distance (FVD) evaluation with multi-extractor support. Leveraging Python and deep learning techniques, Ktank architected the benchmarking framework to integrate I3D, CLIP, and VideoMAE feature extractors, enabling comprehensive and extensible video quality assessment. This approach allowed for consistent cross-model comparisons and facilitated data-driven decisions in model selection and research. The work demonstrated strong skills in Python programming, video processing, and modular system design, resulting in a maintainable tool that accelerates and standardizes evaluation of generated video content.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
2,290
Activity Months1
Your Network
198 peopleSame Organization
@ucsd.edu182
Work History
December 2025
2 Commits • 1 Features
Dec 1, 20252025-12 monthly summary for hao-ai-lab/FastVideo. Focused on delivering a robust video quality benchmarking capability. Implemented Fréchet Video Distance (FVD) benchmarking with multi-extractor support, enabling robust evaluation of generated video quality across I3D, CLIP, and VideoMAE. The work is anchored by commits 55c2e7cd76edc6e23691e7621fd0bd66d6ca940b ([feat] Add fvd implementation (#923)) and 7bfaf82fd766b0e6e4ed0e6773950314c810c412 ([feat] Add new feature extractors for fvd (#954)). Major bugs fixed: none reported this month. Overall impact: accelerated, more reliable evaluation of video generation models, enabling data-driven decisions for model selection and research directions. Demonstrated technologies/skills: Python benchmarking tooling, Fréchet Video Distance metric, multi-extractor integration (I3D, CLIP, VideoMAE), modular architecture for extensible evaluation, version-controlled development with clear PR history.
2 Commits • 1 Features
Dec 1, 20252025-12 monthly summary for hao-ai-lab/FastVideo. Focused on delivering a robust video quality benchmarking capability. Implemented Fréchet Video Distance (FVD) benchmarking with multi-extractor support, enabling robust evaluation of generated video quality across I3D, CLIP, and VideoMAE. The work is anchored by commits 55c2e7cd76edc6e23691e7621fd0bd66d6ca940b ([feat] Add fvd implementation (#923)) and 7bfaf82fd766b0e6e4ed0e6773950314c810c412 ([feat] Add new feature extractors for fvd (#954)). Major bugs fixed: none reported this month. Overall impact: accelerated, more reliable evaluation of video generation models, enabling data-driven decisions for model selection and research directions. Demonstrated technologies/skills: Python benchmarking tooling, Fréchet Video Distance metric, multi-extractor integration (I3D, CLIP, VideoMAE), modular architecture for extensible evaluation, version-controlled development with clear PR history.
December 2025
Activity
Loading activity data...
Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture100.0%
Performance80.0%
AI Usage60.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Deep LearningMachine LearningPython ProgrammingPython scriptingVideo Processingdata analysismachine learningvideo processing
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline