Data Labeling for SmartDetect Computer Vision System
ImageObject Detection
I designed and implemented a multi-stage computer vision pipeline for SmartDetect, focused on fruit detection and classification. This involved labeling and validating images for object detection, classification, and quality grading using YOLOv8 and EfficientNet models. I also created validation datasets and used bounding-box overlays for real-time annotation and feedback. • Labeled images for multi-class fruit detection and classification
• Used bounding boxes for annotating detected objects
• Performed quality grading and size estimation as part of annotation
• Employed OpenCV and PyQt for real-time visualization and export
I designed and implemented a multi-stage computer vision pipeline for SmartDetect, focused on fruit detection and classification. This involved labeling and validating images for object detection, classification, and quality grading using YOLOv8 and EfficientNet models. I also created validation datasets and used bounding-box overlays for real-time annotation and feedback. • Labeled images for multi-class fruit detection and classification • Used bounding boxes for annotating detected objects • Performed quality grading and size estimation as part of annotation • Employed OpenCV and PyQt for real-time visualization and export
2024 - 2024