Israel Waibi

Key Skills

Software

Appen

Argilla

Clickworker

Sama

Scale AI

Top Subject Matter

Self-driving cars imagery

LLM Evaluation in French and English

Image Classification

Top Data Types

Image

Text

Video

Top Task Types

Bounding Box

Cuboid

Data Collection

Entity Ner Classification

Geocoding

Freelancer Overview

With over 4 years progressive experience from Sama, Scale AI, ADRA, in data labeling and AI training data, I have developed a strong expertise in creating high-quality annotated data essential for training machine learning models. Proficient in using specialized tools like Labelbox, I have successfully managed diverse annotation projects, including image, text, and video labeling. My work is characterized by a meticulous attention to detail and a commitment to accuracy, which ensures the integrity and usability of the data.

Labeling Experience

AI Trainer Scale AI (remotasks.com) | Remote, Africa

Scale AI3D SensorBounding BoxPolygon

Project: Self-Driving Cars The project focused on developing and refining machine learning models for self-driving cars, necessitating extensive data labeling to train algorithms that enable autonomous vehicle navigation and decision-making. Tasks: Image Annotation: Labeling objects such as pedestrians, vehicles, traffic signs, and road markings within camera footage. Semantic Segmentation: Classifying each pixel in images to differentiate between different elements, such as road surfaces and obstacles. 3D Bounding Boxes: Annotating 3D objects to provide spatial understanding and depth information. Project Size: Hundreds of thousands of images and video frames were processed and annotated. Typically extended over 8 months. Quality Measures: Accuracy Verification: Regular cross-checks and validation of annotations to ensure precision. Consistency Checks: Adhering to standardized labeling guidelines to maintain uniformity across the dataset. Review Processes Tool Calibration

Project: Self-Driving Cars The project focused on developing and refining machine learning models for self-driving cars, necessitating extensive data labeling to train algorithms that enable autonomous vehicle navigation and decision-making. Tasks: Image Annotation: Labeling objects such as pedestrians, vehicles, traffic signs, and road markings within camera footage. Semantic Segmentation: Classifying each pixel in images to differentiate between different elements, such as road surfaces and obstacles. 3D Bounding Boxes: Annotating 3D objects to provide spatial understanding and depth information. Project Size: Hundreds of thousands of images and video frames were processed and annotated. Typically extended over 8 months. Quality Measures: Accuracy Verification: Regular cross-checks and validation of annotations to ensure precision. Consistency Checks: Adhering to standardized labeling guidelines to maintain uniformity across the dataset. Review Processes Tool Calibration

2020 - 2021

Education

Work History