Hassan Faham

Key Skills

Software

CVAT

Label Studio

OpenCV AI Kit (OAK)

Top Subject Matter

No subject matter listed

Top Data Types

Computer Code Programming

Image

Video

Top Task Types

Classification

Computer Programming Coding

Evaluation Rating

Prompt Response Writing SFT

RLHF

Freelancer Overview

I'm a Computer Vision Engineer & AI Training Specialist with hands-on experience in OCR, object detection, and anomaly segmentation, particularly for industrial quality control. I've developed machine learning models that have significantly improved defect detection and streamlined verification processes across multiple production lines. My work involves integrating industrial cameras, automating inference workflows, and fine-tuning deep learning models to solve real-world problems efficiently. Beyond computer vision, I also specialize in LLM evaluation and AI model training. I’ve assessed and fine-tuned various AI models, from large language models to AI-generated code, helping improve their accuracy and reliability. I’ve reviewed over 200 LLM responses, created detailed guidelines for annotators, and designed high-quality training prompts to enhance AI performance. With expertise in TensorFlow, PyTorch, OpenCV, and YOLO, I take a practical, results-driven approach to AI development, ensuring models are both accurate and efficient.

Labeling Experience

industrial Defect Detection & OCR Annotation for Quality Control

CVATImageBounding BoxSegmentation

Labeled and annotated industrial images for defect detection and OCR to improve automated quality control systems. Tasks included: • Bounding box annotation for defect localization. Classification of defects into categories (e.g., scratches, misprints, missing components). • Segmentation of specific anomalies for precise defect detection. • OCR annotation for serial number recognition and validation. • Quality assurance and dataset refinement to improve model accuracy and reduce false positives.

Labeled and annotated industrial images for defect detection and OCR to improve automated quality control systems. Tasks included: • Bounding box annotation for defect localization. Classification of defects into categories (e.g., scratches, misprints, missing components). • Segmentation of specific anomalies for precise defect detection. • OCR annotation for serial number recognition and validation. • Quality assurance and dataset refinement to improve model accuracy and reduce false positives.

2023

Education

Work History