Information Technology Ai Intern

CONTACT

• **************@*****.***

• https://github.com/KhanhMinh2004

• 093*******

EDUCATION

UNIVERSITY OF SCIENCE AND

EDUCATION, THE UNIVERSITY

OF DA NANG

GPA: 3.2

Bui Tran

Khanh Minh

AI INTERN

I’m a fourth-year student who is interested in Artificial Intelligent (AI). I really want to join an internship in a dynamic environment to learn, gain experience and improve my skills. I hope I will have an opportunity to show myself TECHNICAL SKILLS

• Languages: Python, Sql

• Developer Tools: Git, Pycharm, VS

Code, Google Colab, Jupyter Notebook,

Ubuntu Linux, Docker

• Image Processing: Augmentation

• Libraries & Frameworks:

- AI/ML: TensorFlow, Keras, Scikit-

learn, Ultralytics YOLO, PaddleOCR,

Pytorch

- Image Processing: OpenCV, Pillow,

Data Augmentation, Matplotlib

- Data Handling: NumPy, Pandas

- GUI & OS: Tkinter, Subprocess, OS,

Time

• APIs & Platforms: Kaggle API, Google

Colab, Google Drive API

LANGUAGES

ENGLISH

• Reading and writing: Intermediate level

• Listening: Intermediate level

• Speaking: Basic level

PROJECT

Real-Time Face Recognition System

Aug. 2024 – Present

• Developed a real time face recognition system using Haar Cascade for detection and custom CNN for classification.

• Achieved 90% accuracy for static faces and 77% for moving faces in real-world webcam conditions.

• Built a full image preprocessing pipeline: face detection, normalization, and live classification.

• The CNN model was trained using real face images. It learned through multiple convolution, pooling and dropout layers and optimized using appropriate algorithms to achieve high performance.

• Github repo: https://github.com/KhanhMinh2004/detect_face_project Real-Time Acne Detection System using YOLOv8

Mar 2025 – Present

• Built a real-time object detection system to identify acne spots using YOLOv8 large model.

• Collected and fine-tuned a pretrained YOLOv8l model on Kaggle acne detection dataset in YOLOv8 format.

• Achieved the detector highly under realistic webcam-based facial conditions.

• Fine-tuned YOLOv8l on over 900 labeled facial images with acne using YOLOv8 format; trained over 100 epochs.

ID Card Information Extraction and Face Verification using PaddleOCR & DeepFace

Jan. 2025 – Present

• Built a Python pipeline to extract Vietnamese citizen ID card data using PaddleOCR.

• Resized ID card images to a fixed resolution (960 720), enduring the card was captured and aligned properly to enable accurate region cropping for OCR extraction (e.g., name, date of birth, ID number).

• Applied DeepFace to verify identity between the ID card photo and a real-world image, using RetinaFace for face detection and ArcFace for face recognition by computing similarity between face embeddings

Fine-tuning LLaMA 3.2 using Unsloth for Efficient Instruction Tuning Nov. 2024 – Present

• Customized and fine-tuned Meta LLaMA 3.2 3B-Instruct using Unsloth for faster and memory-efficient training on consumer GPUs

• Integrated Medical-Sci-Instruct-1M dataset with ShareGPT format for supervised fine-tuning (SFT).

• Implemented instruction-response fine-tuning using trl and Hugging Face Transformers with custom chat templates

• Leveraged 4-bit quantization (bnb-4bit) and parameter-efficient fine- tuning to train 0.81% of parameters on a single GPU with 2x faster speed using Unsloth

ACTIVITIES

• Actively participated in university activities.

• Experienced working in an English-speaking environment.

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