AabidMK/Object-Recognition-System__Infosys_Internship_Feb2025
Apr 2025 – Apr 2025
1 Month active
Languages Used
Python
Technical Skills
Computer VisionObject DetectionObject SegmentationOpenCVPyTorchStreamlit
indu-keerthika-3
PROFILE
Indu-keerthika-3
Inditha Niranjan developed a YOLOv8 Streamlit Object Detection and Segmentation App for the AabidMK/Object-Recognition-System__Infosys_Internship_Feb2025 repository. The project focused on enabling multi-input support, allowing users to process images, videos, webcam streams, RTSP feeds, and YouTube videos through a unified Streamlit interface. Inditha used Python, OpenCV, and PyTorch to integrate the pretrained yolov8n.pt model, providing immediate evaluation and prototyping capabilities. The app featured a toggle for switching between detection and segmentation modes, along with a reusable input-adapter and visualization workflow. This work established a scalable foundation for future computer vision model integrations and stakeholder demonstrations.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
111
Activity Months1
Your Network
7 peopleWork History
April 2025
2 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary: Delivered the YOLOv8 Streamlit Object Detection/Segmentation App as part of the AabidMK/Object-Recognition-System__Infosys_Internship_Feb2025 project. The feature enables multi-input object detection and segmentation via a streamlined Streamlit interface, with support for images, videos, webcam, RTSP, and YouTube inputs, plus a toggle to switch between detection and segmentation and visualization of results. The pretrained model yolov8n.pt was included to enable immediate evaluation and prototyping. Two initial commits were used to add feature files and instantiate the repository for this capability.
2 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary: Delivered the YOLOv8 Streamlit Object Detection/Segmentation App as part of the AabidMK/Object-Recognition-System__Infosys_Internship_Feb2025 project. The feature enables multi-input object detection and segmentation via a streamlined Streamlit interface, with support for images, videos, webcam, RTSP, and YouTube inputs, plus a toggle to switch between detection and segmentation and visualization of results. The pretrained model yolov8n.pt was included to enable immediate evaluation and prototyping. Two initial commits were used to add feature files and instantiate the repository for this capability.
April 2025
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Quality Metrics
Correctness90.0%
Maintainability90.0%
Architecture90.0%
Performance90.0%
AI Usage40.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Computer VisionObject DetectionObject SegmentationOpenCVPyTorchStreamlitYOLOv8YouTube API
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