Asher Osekeny

Key Skills

Software

CrowdSource

CVAT

Data Annotation Tech

Google Cloud Vertex AI

Top Subject Matter

self driving car imagery and real time inventory verification

LLM evalution in english

image classification and identification

Top Data Types

Document

Geospatial Tiled Imagery

Image

Top Task Types

Action Recognition

Computer Programming Coding

Data Collection

Emotion Recognition

Evaluation Rating

Freelancer Overview

I have extensive experience in data labeling and AI training data, having worked on numerous projects that involve annotating large datasets for machine learning models. My key skills include meticulous attention to detail, proficiency in various annotation tools, and a deep understanding of data quality standards. I have contributed to projects ranging from image and text annotation to audio and video labeling, ensuring high accuracy and consistency in the labeled data. One of my standout projects involved leading a team to label a diverse dataset for a natural language processing model, which significantly improved the model's performance in understanding and generating human-like text. My qualifications are further enhanced by my ability to adapt to different data types and labeling requirements, making me a versatile and reliable contributor to any AI training data project.

Labeling Experience

Logistify AI Data Annotation for Inventory Verification and Car recogniton for AVII AI

CVATImageObject DetectionText Summarization

1. Setup and Installation - Prerequisites: Ensure you have Docker, Docker Compose, and Git installed on your system. - Clone Repository: Clone the CVAT repository from GitHub. - Run CVAT: Use Docker Compose to start CVAT in detached mode. 2. Creating an Annotation Task - Upload Dataset: Import your images or videos into CVAT. - Define Task: Create a new annotation task by specifying the dataset and annotation type (e.g., bounding boxes, polygons, points). 3. Annotating Data - Annotation Interface: Use CVAT's intuitive interface to label objects in your images or videos. - Tools and Features: Utilize various tools like bounding boxes, polygons, and key points to accurately annotate your data. - Attributes: Add attributes to your annotations for more detailed labeling.

1. Setup and Installation - Prerequisites: Ensure you have Docker, Docker Compose, and Git installed on your system. - Clone Repository: Clone the CVAT repository from GitHub. - Run CVAT: Use Docker Compose to start CVAT in detached mode. 2. Creating an Annotation Task - Upload Dataset: Import your images or videos into CVAT. - Define Task: Create a new annotation task by specifying the dataset and annotation type (e.g., bounding boxes, polygons, points). 3. Annotating Data - Annotation Interface: Use CVAT's intuitive interface to label objects in your images or videos. - Tools and Features: Utilize various tools like bounding boxes, polygons, and key points to accurately annotate your data. - Attributes: Add attributes to your annotations for more detailed labeling.

2022 - 2023

Education

Work History