AabidMK/Object-Recognition-System__Infosys_Internship_Feb2025
Mar 2025 – Mar 2025
1 Month active
Languages Used
Python
Technical Skills
Computer VisionObject DetectionPythonStreamlitVideo ProcessingYOLO
Keerthana-Bethi
PROFILE
Keerthana-bethi
Developed and delivered a YOLO Object Detection Toolkit for the AabidMK/Object-Recognition-System__Infosys_Internship_Feb2025 repository, focusing on real-time object detection in video streams. The solution combined Python and Streamlit to provide an interactive user interface supporting video uploads, live camera feeds, object tracking, alerting, and export of detection history. The engineering work included end-to-end workflow validation, ensuring seamless progression from video input to detection and alert generation. Legacy code was streamlined by removing outdated UI components, reducing maintenance complexity. The project demonstrated practical application of computer vision, object detection, and video processing skills, resulting in a user-focused, maintainable toolkit.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
1
Lines of code
462
Activity Months1
Your Network
7 peopleWork History
March 2025
3 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary focusing on key accomplishments for the AabidMK/Object-Recognition-System__Infosys_Internship_Feb2025 repository. The primary delivery was the YOLO Object Detection Toolkit, including a Python script for real-time YOLO-based object detection on video and a new Streamlit UI that supports interactive recognition/segmentation with upload, live camera, tracking, alerts, and history export. In addition, legacy UI code was cleaned up by removing final_streamlit.py, reducing maintenance burden and potential user confusion. The work demonstrates end-to-end feature development, codebase cleanup, and user-focused tooling that accelerates detection, alerting, and historical analysis.
3 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary focusing on key accomplishments for the AabidMK/Object-Recognition-System__Infosys_Internship_Feb2025 repository. The primary delivery was the YOLO Object Detection Toolkit, including a Python script for real-time YOLO-based object detection on video and a new Streamlit UI that supports interactive recognition/segmentation with upload, live camera, tracking, alerts, and history export. In addition, legacy UI code was cleaned up by removing final_streamlit.py, reducing maintenance burden and potential user confusion. The work demonstrates end-to-end feature development, codebase cleanup, and user-focused tooling that accelerates detection, alerting, and historical analysis.
March 2025
Activity
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Quality Metrics
Correctness86.6%
Maintainability86.6%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Computer VisionObject DetectionPythonStreamlitVideo ProcessingYOLO
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