Diet Maintenance using Computer Vision – Data Labeler/Annotator
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I applied image pre-processing, data labeling, and deep learning model training for automatic food classification using the Yolov5 framework. My work involved collecting, labeling, and validating food images to improve model accuracy and performance. This process enhanced detection accuracy by 20% through optimized data annotation strategies and hyperparameter tuning.
• Built and deployed custom web scrapers in Python to gather image datasets for labeling.
• Conducted meticulous data labeling and preprocessing ensuring high data quality for model training.
• Utilized PyTorch and Yolov5 for model development, integration, and evaluation.
• Developed and integrated APIs for image upload, storage, and downstream processing.
I applied image pre-processing, data labeling, and deep learning model training for automatic food classification using the Yolov5 framework. My work involved collecting, labeling, and validating food images to improve model accuracy and performance. This process enhanced detection accuracy by 20% through optimized data annotation strategies and hyperparameter tuning. • Built and deployed custom web scrapers in Python to gather image datasets for labeling. • Conducted meticulous data labeling and preprocessing ensuring high data quality for model training. • Utilized PyTorch and Yolov5 for model development, integration, and evaluation. • Developed and integrated APIs for image upload, storage, and downstream processing.
2021 - 2022