Object Detection Using YOLO
Object detection project using YOLO (You Only Look Once) algorithm, applied in Computer Vision tasks with OpenCV.
I am an IT student at Saigon University (SGU). I have a deep passion for Machine Learning, specifically Deep Learning for Computer Vision. I enjoy exploring how machines understand and process visual data just like humans do.
Focus
AI / ML
Student at
SGU
Overview of GitHub activity, contribution streaks, and most used languages.
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Quick Stats
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15+
Public Repos
AI/ML
Primary Focus
2025
Active Year
5+
Languages
Most Used Languages
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🏆 GitHub Achievements
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📊 Contribution Activity Graph
1,025+ contributions in the last year
A collection of my work in AI/ML, Web Development, and University Projects.
Object detection project using YOLO (You Only Look Once) algorithm, applied in Computer Vision tasks with OpenCV.
Comprehensive software testing deliverables for the SGU clothing e‑commerce project: Dockerized app, CI/CD pipelines, deployments to Vercel & Railway. Includes test artifacts (DOCX, Excel test cases), and full architecture & behavior diagrams (C4, Activity, Sequence, Deployment, DFD, ERD). Test suites cover black‑box and white‑box approaches.
A simple web demo for English→French translation using an open-source model. Containerized with Docker for easy deployment.
Building Convolutional Neural Networks (CNNs) using TensorFlow and Keras to classify images into different categories with high accuracy.
System using Natural Language Processing (NLP) and machine learning with Scikit-learn to detect and classify fake news articles.
Housing price prediction model based on dataset attributes, utilizing regression algorithms for accurate estimations.
Hugging Face
Advanced NLP project for multilingual translation and text generation using Transformer models. Supports multiple languages and includes both training and inference pipelines.
Automated tools to scrape data from websites using Selenium and build predictive models based on the collected data.
A website project designed for a fabric agency, showcasing products and agency information.
Visual Code Online is a browser-based code editor powered by Monaco and Vite, offering live preview, multi-file project management and auto-save. It includes an AI Code Copilot that indexes the repository with Jina embeddings, performs semantic retrieval with Faiss, and uses Google Gemini to generate context-aware answers about the code.
A web-based face recognition system using deep learning models to identify and verify faces in real-time. Developed with PyTorch for model inference and OpenCV for image processing.
[Image of face recognition technology workflow using PyTorch]
A food ordering application project. Developed in collaboration with team members.
Various academic projects including Distributed Databases, Software Engineering (KTPM), and Python assignments.
Built a Retrieval-Augmented Generation (RAG) system using open-source Large Language Models from Hugging Face such as Ollama, Qwen, Mistral, and Tiny LLM. Integrated BLIP for Visual Question Answering (VQA) and image captioning tasks, with the entire system containerized using Docker for scalable deployment.
An intelligent AI Agent capable of performing calculations, plotting graphs, analyzing data, and searching for the latest information. The agent leverages models from Google, Meta, Microsoft, and Alibaba.
Participated in various AI/ML competitions and hackathons, applying cutting-edge technologies to solve real-world problems.
Developed an intelligent meeting assistant platform leveraging Computer Vision for participant recognition, OCR for document processing, Large Language Models for content analysis, and AI Agents for automated task management. The system includes audio recording capabilities to capture and transcribe meeting discussions in real-time.
Status: Submitted proposal - Did not advance to final round
Developed an autonomous UAV (Unmanned Aerial Vehicle) system for detecting people in danger during flood disasters. Using advanced computer vision and object detection algorithms, the system can identify individuals trapped in flood zones from aerial imagery, enabling faster emergency response and rescue operations. This project combines drone technology with AI to save lives in critical situations.
Impact: Real-time detection system for emergency disaster response
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