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
Developed a YOLOv8-based object detection and segmentation application using Python and Streamlit, delivered as part of 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 interface. Leveraging OpenCV and PyTorch, the solution integrated a pretrained yolov8n.pt model for immediate evaluation and prototyping. The implementation included a reusable input-adapter and visualization workflow, streamlining future computer vision model integrations. This work established a scalable, ready-to-use prototype for rapid stakeholder feedback and demonstration across diverse computer vision input sources.
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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