Saeed Jalingo

Key Skills

Software

CVAT

Labelbox

LabelImg

Label Studio

Roboflow

Top Subject Matter

No subject matter listed

Top Data Types

Image

Text

Video

Top Label Types

Bounding Box

Classification

Data Collection

Emotion Recognition

Land Cover Classification

Object Detection

Segmentation

Tracking

Freelancer Overview

I am a detail-oriented AI Data Annotator with hands-on experience preparing high-quality training data for machine learning models. At Cerebro Systems in Abuja, I annotated image and video datasets used for robotics navigation and object detection systems. My work included bounding box annotation, semantic segmentation, and keypoint labeling, ensuring datasets were accurately labeled to support reliable AI model training. I also worked with LiDAR and sensor-based datasets to support robotic perception systems while conducting data quality assurance (QA) and validation checks to maintain high annotation accuracy. In addition, I have experience supporting AI training workflows for Large Language Models (LLMs) and Natural Language Processing (NLP) tasks such as text classification, data labeling, and dataset quality review. I collaborated with engineers to refine annotation guidelines and improve dataset consistency while consistently meeting productivity and accuracy benchmarks under tight project deadlines. My background in Artificial Intelligence fundamentals and strong attention to detail enables me to deliver precise, reliable training data for AI development.

Labeling Experience

Cattle Detection and Livestock Monitoring Dataset Annotation

CVATImageBounding BoxSegmentation

Annotated large-scale image datasets for a livestock monitoring system designed to detect and track cattle in farm environments. Tasks included drawing accurate bounding boxes around cattle across different conditions such as varying lighting, occlusion, and herd density. The project supported the training of computer vision models used for automated livestock counting, movement tracking, and behavior analysis. Over 3500 images were labeled with strict quality control procedures, including annotation review, dataset consistency checks, and adherence to detailed labeling guidelines. Particular attention was given to edge cases such as partially visible animals and overlapping objects to ensure reliable model training data.

Annotated large-scale image datasets for a livestock monitoring system designed to detect and track cattle in farm environments. Tasks included drawing accurate bounding boxes around cattle across different conditions such as varying lighting, occlusion, and herd density. The project supported the training of computer vision models used for automated livestock counting, movement tracking, and behavior analysis. Over 3500 images were labeled with strict quality control procedures, including annotation review, dataset consistency checks, and adherence to detailed labeling guidelines. Particular attention was given to edge cases such as partially visible animals and overlapping objects to ensure reliable model training data.

2025 - 2025

Urban Traffic Object Detection Annotation

CVATVideoBounding BoxSegmentation

Annotated urban street images and short video sequences to support computer vision models used in traffic monitoring and smart city infrastructure. The dataset included objects such as vehicles, pedestrians, bicycles, traffic lights, and road signs. Tasks involved bounding box annotation and segmentation to ensure accurate object localization and classification. Quality assurance processes included annotation validation, guideline compliance checks, and cross-review to maintain dataset reliability for model training.

Annotated urban street images and short video sequences to support computer vision models used in traffic monitoring and smart city infrastructure. The dataset included objects such as vehicles, pedestrians, bicycles, traffic lights, and road signs. Tasks involved bounding box annotation and segmentation to ensure accurate object localization and classification. Quality assurance processes included annotation validation, guideline compliance checks, and cross-review to maintain dataset reliability for model training.

2024 - 2024

Education

Work History