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LabelImgRoboflowProdigyNo subject matter listed
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TextI am an AI specialist and software engineer with hands-on experience in high-precision data annotation and end-to-end dataset preparation for machine learning and computer vision projects. My expertise includes labeling images with bounding boxes and polygons, achieving a 98% accuracy rate, and developing Python scripts to automate data cleaning and de-duplication, ensuring the highest quality of training data. I have worked extensively with tools like CVAT and LabelImg, and have managed data pipelines for deep learning models in real-world applications, such as coconut classification and IoT pothole detection using YOLO and EfficientNet. My background in both AI research and network engineering allows me to bridge technical gaps and deliver reliable, scalable AI solutions.
saya memberikan label objek pada gambar, serta memberikan koordinat objek, setelah itu saya mendeskripsikan gambar berdasarkan objek yang di labelkan
As Lead Developer for the IoT Pothole Detection System, I annotated and validated a large-scale dataset of road surface images for a YOLO object detection model. The work required precise bounding box placement and detailed categorization of pothole images. I managed the entire data pipeline from image collection to dataset preparation. • Annotated high-resolution road surface images for model training • Employed bounding box labeling techniques using LabelImg, CVAT, and Roboflow • Enabled high-accuracy pothole detection and categorization • Coordinated data collection on edge devices and prepared model-ready datasets
As a Machine Learning Researcher for the Coconut Classification Project, I curated and labeled a specialized dataset for coconut quality and size. I applied various image augmentation techniques and performed rigorous auditing to correct mislabeled samples. The work ensured high label accuracy and increased dataset diversity. • Labeled images for coconut quality and size classification tasks • Utilized Roboflow and CVAT to annotate and validate data • Achieved over 95% label accuracy via manual review and correction • Supported model training using EfficientNet and Transfer Learning
Bachelor of Computer Science, Computer Science and Information Technology
Network Engineering Intern
