April Williams

Key Skills

Software

Labelbox

Top Subject Matter

No subject matter listed

Top Data Types

Image

Video

Top Label Types

Bounding Box

Point Key Point

Segmentation

Tracking

Classification

Emotion Recognition

Freelancer Overview

I am an experienced AI Training Specialist with over 3 years of hands-on expertise in data labeling and annotation for machine learning and computer vision projects. My background includes working with large-scale image, video, and audio datasets, where I have performed tasks such as bounding box annotation, segmentation, object tracking, and audio transcription to support model training in domains like autonomous systems and speech recognition. I am highly skilled in using industry-standard tools including Labelbox, CVAT, YOLO annotation formats, Supervisely, Roboflow, and Audacity, and have a strong focus on accuracy, quality assurance, and dataset validation. I excel at collaborating with ML engineers to refine annotation guidelines and ontologies, and am comfortable managing complex workflows, tight deadlines, and large volumes of data to ensure consistent, high-quality results.

Labeling Experience

High-precision multimodal data annotation for computer vision model

LabelboxVideoBounding BoxClassification

Performed high-precision video annotation for computer vision model training across diverse real-world scenarios. The project involved frame-by-frame labeling of moving objects using bounding boxes, polygons, and keypoints, with a strong emphasis on multi-object tracking consistency across long video sequences. Responsibilities included identifying and labeling dyamic entities such as people, vehicles and activities while preserving object IDs throughout occlusions, motion blur and scene transitions. Processed large batches of video data tools including labelbox and CVAT, preparing datasets optimized for YOLO-based detection and tracking pipelines.

Performed high-precision video annotation for computer vision model training across diverse real-world scenarios. The project involved frame-by-frame labeling of moving objects using bounding boxes, polygons, and keypoints, with a strong emphasis on multi-object tracking consistency across long video sequences. Responsibilities included identifying and labeling dyamic entities such as people, vehicles and activities while preserving object IDs throughout occlusions, motion blur and scene transitions. Processed large batches of video data tools including labelbox and CVAT, preparing datasets optimized for YOLO-based detection and tracking pipelines.

2024

High-Precision Multimodal Data Annotation for Computer Vision Models

LabelboxImageBounding BoxPoint Key Point

Executed high-accuracy data labeling and annotation for large-scale multimodal datasets supporting computer vision and audio intelligence models. Responsibilities included creating bounding boxes, polygons and keypoints for object detection tasks, performing frame by frame multi-object tracking in video sequences and classifying as well as transcribing audio data. Worked with diverse datasets including street scenes, human activities, and general object categories. Processed thousands of image frames and extended video sequences, optimizing annotations for YOLO-based detection models. Conducted self-review and peer QA checks to minimize relabeling rates and improve datasets consistency.

Executed high-accuracy data labeling and annotation for large-scale multimodal datasets supporting computer vision and audio intelligence models. Responsibilities included creating bounding boxes, polygons and keypoints for object detection tasks, performing frame by frame multi-object tracking in video sequences and classifying as well as transcribing audio data. Worked with diverse datasets including street scenes, human activities, and general object categories. Processed thousands of image frames and extended video sequences, optimizing annotations for YOLO-based detection models. Conducted self-review and peer QA checks to minimize relabeling rates and improve datasets consistency.

2022 - 2024

Education

Work History