AabidMK/Object-Recognition-System__Infosys_Internship_Feb2025
Feb 2025 – Feb 2025
1 Month active
Languages Used
Python
Technical Skills
Data VisualizationFile System OperationsRandom Sampling
ashikjoel
PROFILE
Ashikjoel
Developed a data-exploration feature for the object-recognition project, focusing on the COCO 2017 Dataset Image Preview within the AabidMK/Object-Recognition-System__Infosys_Internship_Feb2025 repository. This tool, implemented in Python, enables users to quickly visualize random samples from the test and validation sets, supporting efficient dataset inspection and early-stage model debugging. The approach incorporated robust file system operations to validate dataset directory existence and handle empty datasets, while also providing per-dataset image count reporting for rapid sizing assessments. The work emphasized data visualization and random sampling, establishing a foundation for future dataset quality checks and streamlined Colab-based development workflows.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
371
Activity Months1
Your Network
7 peopleWork History
February 2025
2 Commits • 1 Features
Feb 1, 2025February 2025: Delivered a focused data-exploration feature for the object-recognition project by implementing the COCO 2017 Dataset Image Preview. This utility enables quick inspection of the dataset by displaying random images from COCO 2017 test and validation sets, while ensuring robust input handling and informative reporting. The work provides a foundation for dataset sanity checks and model debugging in the development workflow, with Colab-originated origin markers for traceability across environments.
2 Commits • 1 Features
Feb 1, 2025February 2025: Delivered a focused data-exploration feature for the object-recognition project by implementing the COCO 2017 Dataset Image Preview. This utility enables quick inspection of the dataset by displaying random images from COCO 2017 test and validation sets, while ensuring robust input handling and informative reporting. The work provides a foundation for dataset sanity checks and model debugging in the development workflow, with Colab-originated origin markers for traceability across environments.
February 2025
Activity
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Quality Metrics
Correctness60.0%
Maintainability60.0%
Architecture60.0%
Performance60.0%
AI Usage60.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Data VisualizationFile System OperationsRandom Sampling
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