dataforgoodfr/13_reveler_inegalites_cinema
Mar 2025 – Mar 2025
1 Month active
Languages Used
Python
Technical Skills
Computer VisionData ProcessingDeep LearningMachine LearningPyTorchPython
Julien-Commes
PROFILE
Julien-commes
Julien Commes developed an end-to-end machine learning pipeline for the dataforgoodfr/13_reveler_inegalites_cinema repository, focusing on automated trailer analysis. Leveraging Python, PyTorch, and computer vision techniques such as YOLO, Julien implemented a workflow that extracts frames from movie trailers, detects and classifies faces, filters and clusters results, and stores predictions directly on the output video. This approach reduced the need for manual review and enabled scalable research into representation within cinema. The pipeline’s reproducible and extensible design, along with improved code maintainability, provided a robust foundation for video data processing and content indexing in the project.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
1,076
Activity Months1
Your Network
12 peopleShared Repositories
12
Audrey DuranMember
FitzfoufouMember
Florian_YunMember
hadrien PoinseauxMember
ndretexMember
LudivineLacourMember
Nicolas RevelMember
Paul GabrielMember
lina kaciMember
Work History
March 2025
1 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for dataforgoodfr/13_reveler_inegalites_cinema: Delivered the V1 Trailer Analysis ML Pipeline, enabling end-to-end processing of movie trailers to detect and classify individuals, with predictions stored on the output video. This supports scalable research into representation and supports content indexing. Core achievements: implemented frame extraction, face detection, filtering, classification, and clustering; integrated storage of predictions; improved code comments for maintainability; established a reproducible ML pipeline workflow. No major bugs fixed this period. Business impact: reduces manual review, accelerates data collection, enabling data-driven insights into cinema representations; technical impact: robust end-to-end pipeline with extensible design.
1 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for dataforgoodfr/13_reveler_inegalites_cinema: Delivered the V1 Trailer Analysis ML Pipeline, enabling end-to-end processing of movie trailers to detect and classify individuals, with predictions stored on the output video. This supports scalable research into representation and supports content indexing. Core achievements: implemented frame extraction, face detection, filtering, classification, and clustering; integrated storage of predictions; improved code comments for maintainability; established a reproducible ML pipeline workflow. No major bugs fixed this period. Business impact: reduces manual review, accelerates data collection, enabling data-driven insights into cinema representations; technical impact: robust end-to-end pipeline with extensible design.
March 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture90.0%
Performance80.0%
AI Usage80.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Computer VisionData ProcessingDeep LearningMachine LearningPyTorchPythonVideo AnalysisYOLO
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