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
Ashik Joel developed a data-exploration feature for the AabidMK/Object-Recognition-System__Infosys_Internship_Feb2025 repository, focusing on the COCO 2017 Dataset Image Preview utility. Using Python, he combined data visualization, file system operations, and random sampling to enable quick inspection of test and validation images. His approach included robust validation for dataset directory existence and empty datasets, along with automated reporting of image counts to support dataset sizing and quality checks. By documenting Colab-originated commits for traceability, Ashik laid a foundation for future dataset exploration tools, providing practical support for model debugging and data quality assurance in object recognition 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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