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
Developed a robust video quality benchmarking module for the hao-ai-lab/FastVideo repository, focusing on implementing Fréchet Video Distance (FVD) evaluation with support for multiple feature extractors. Leveraging Python and deep learning frameworks, the work introduced a modular architecture that enables consistent assessment of generated video quality using I3D, CLIP, and VideoMAE extractors. This approach allows for extensible, cross-model comparisons and supports data-driven decisions in model selection and research. The solution accelerated and standardized video generation evaluation workflows, emphasizing maintainability and scalability. No major bugs were reported, reflecting a focus on feature delivery and reliable, version-controlled development practices.
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
213 peopleSame Organization
@ucsd.edu195
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