Anup Dhakal
I'm
About
I am Anup Dhakal currently studying computer enginering in Khwopa College of Engineering in eighth Semester.
- Birthday: 23 December 2001
- Birth Place: Tansen,Palpa
- Current Address Kathmandu
- Website: anupdhakal1.com.np
- Age:
- Degree: Bachelor's
- Email: [email protected]
- Freelance: Available
Skills
Resume
A forward-thinking and technically skilled individual dedicated to solving complex problems with innovative solutions. Passionate about computer vision, machine learning, and software development, with hands-on experience in YOLO and traffic management systems.
Summary
Anup Dhakal
Proactive and detail-oriented final-year student. Experienced in YOLO, OpenCV, PyTorch, and Python development.
Education
Bachelor of Computer Engineering
2020-Present
Khwopa College of Engineering, Bhaktapur,Bagmati State
Focused on developing innovative solutions for real-world problems, with a specialization in machine learning, computer vision, and intelligent systems. Worked on multiple projects involving video processing, dataset management, and deep learning.
Science Faculty
2018-2020
St.Xavier's School ,Jawalakhel
School
2011-2018
St. Capitanio School,Palpa
Python Developer (Freelance)
2024-2025
Self-Employed
- Implemented advanced video processing techniques using OpenCV, focusing on masking and blending video elements.
- Designed scripts for Google Colab and Kaggle environments to preprocess and analyze datasets efficiently.
- Contributed to multiple projects requiring custom modifications of VGG16 and other PyTorch-based models.
Projects
NEPSE Stock Prediction using LSTM
Minor project done as of requirement of Computer Engineering Sixth Semester Curriculum which is based on Deep Learning model LSTM.
SmartFlow: Intelligent Traffic Management System for Speed Monitoring and Adaptive Signal Control
Done as a major project for fourth year of Engineering. Developed a vehicle detection and tracking system using YOLOv8 + DeepSORT.Integrated speed estimation using real-world distance and video FPS.
Image Generation using GANs
Implemented Generative Adversarial Networks (GANs) to generate realistic male and female faces. Trained the model on a diverse dataset to improve image quality and diversity.