Wang M.

Key Skills

Software

Roboflow

Top Subject Matter

Computer vision object detection dataset annotation (YOLO)

Top Data Types

Image

Top Task Types

Object Detection

Bounding Box

Point/Key Point

Segmentation

Freelancer Overview

Computer Vision Data Labeling & YOLO Training-Oriented Dataset Production. Brings 2+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Roboflow. AI-training focus includes data types such as Image and labeling workflows including Object Detection, Segmentation, and Bounding Box.

Labeling Experience

Computer Vision Annotation Freelancer - Self-Employed

ImageBounding BoxObject Detection

Worked as a computer vision data annotation freelancer supporting YOLO object detection dataset production and delivery. Ensured annotations matched industry specifications and training requirements through systematic quality checks and corrections. Coordinated batch task timelines and incorporated revision feedback to maintain consistent label accuracy for downstream model training. • Produced YOLO-ready annotations including bounding boxes, polygons, and keypoints • Performed self-review to reduce boundary offset, occlusion, and category-label inconsistencies • Applied standardized rules for stable annotation quality across batches • Managed revisions and delivered training-ready data on schedule

2025 - Present

Data Annotation Freelancer (Computer Vision Data Annotation)

RoboflowImageObject Detection

Performed YOLO-based computer vision annotation for object detection datasets, including 2D bounding box labeling. Ensured label quality by correcting boundary offset issues, handling occlusion labeling, and standardizing inconsistent category labels for training readiness. Followed industry-standard annotation specifications and batch delivery rules with self-checking and revision acceptance to reduce iteration costs. • Labeled industrial parts, daily objects, and varied visual detection scenarios • Used standardized dataset splitting concepts (train/val/test) and YOLO-compatible formatting • Conducted raw data cleaning, image quality inspection, and category standardization • Managed annotation batches with on-time delivery and feedback-driven revisions

2025 - Present

Multi-Format Visual Annotation for Training Datasets

RoboflowImageSegmentation

Delivered multi-task visual annotations optimized for model training in computer vision projects. • Labeled polygon instance segmentation targets using consistent annotation standards. • Added keypoint/landmark annotations for structured pose or feature learning. • Executed batch cleaning steps including deduplication and correction of annotation errors. • Verified dataset availability through local model training to confirm annotation suitability for formal training.

Present

Computer Vision Data Labeling & YOLO Training-Oriented Dataset Production

RoboflowImageObject Detection

Produced computer vision datasets intended for YOLO model training using Roboflow end-to-end workflows. • Performed 2D bounding box labeling and detection dataset preparation for downstream local training. • Conducted dataset filtering, deduplication, noise removal, and annotation error correction prior to export. • Applied train/val/test splitting and YOLO-format export rules to maintain training usability. • Implemented label quality evaluation standards with a target accuracy above 95% for delivery.

Present

Education

Work History