11-Retrieving User
This tutorial is all about Retrieving User. In this tutorial, I will teach you how to retrieve the user’s records in the MySQL Database. with this, the records will be displayed …
10-Register New User
This tutorial is all about Register New User. In this tutorial, I’m going to teach you how to register a new user in the User’s Registration Form. with this, you can …
09-Creating Manage User Form
This tutorial is all about Creating Manage User Form. After finishing the last topic for creating the Employee’s Registration Form. Now, we’re going to focus in creating a Manage User’s Form. …
08-Removing Employees
This tutorial is all about Removing Employees. Today, I’m going to teach how to remove the Employee’s Information in the MySQL Database. With this you can remove the Information of the …
07-Updating Employees
This tutorial is all about Updating Employees. In this tutorial, I will teach you how to update the records of the Employee’s Information. With this, you can update the records of …
06-Search Employees
This tutorial is all about Search Employees. In this tutorial, I will teach you how to search the record of the Employee in the database. With this, you can search the …
05-Retrieving Employees
This tutorial is all about Retrieving Employees. Today, I’m going to teach you how to retrieve data in the database. Retrieving data means that, the data obtain from a database management …
04-Register New Employee
This tutorial is all about Register New Employee. In this tutorial, I will teach you how to create an Employee’s Registration Form. With this, you can register new Employee and you …
03-Setting Up the Database Connection Between MySQL and Visual Basic
This tutorial is all about Setting Up the Database Connection Between MySQL and Visual Basic. In this tutorial, I will teach you how to set up the database connection in the …
02-Creating Database and Setting Up tables
This tutorial is all about Creating Database and Setting Up tables. Now, I’m going to start this tutorial on how to create a database and set up the tables that are …
01-Introduction to the Employees Information System
This post is all about Introduction to the Employees Information System. This time, I want to create an Employees Information System and I will do this in phase to phase basis. I’m going …
How to Convert an Audio File to a Text In VB.net
How to Convert an Audio File to a Text In VB.net In this tutorial I will show how to convert an audio file into a text by using Visual Basic …
Frequently Asked Questions
Are these deep learning projects free for capstone and thesis use?
Yes. All deep learning projects on this hub are free to download, modify, and submit. No attribution required for academic use. Most are MIT-licensed or include source-code packs with sample datasets and pretrained model weights.
What deep learning frameworks do I need installed?
Most projects use OpenCV (cv2) for video capture and image preprocessing, plus one of: TensorFlow / Keras (Caffe model loading via cv2.dnn, custom CNN training), PyTorch (research-style models, YOLO v5+, transformers), or MediaPipe (Google's optimized face/hand/pose detectors). Install with pip install opencv-python tensorflow keras torch torchvision mediapipe numpy. Python 3.10, 3.11, or 3.12 recommended (avoid 3.13 until all wheels catch up).
Do I need a GPU to run these deep learning projects?
For inference (running a pretrained model on your webcam): no, CPU runs at 15-30 FPS for most computer-vision tasks. For training a custom model on your own dataset: GPU strongly recommended (CPU works but is slow). Free GPU options: Google Colab Free (12-hour sessions, sufficient for most BSIT capstones), Kaggle Notebooks Free (30-hour weekly quota), Paperspace Free tier. No need to buy a $1000+ GPU just for a capstone defense.
Deep learning vs classical machine learning, which should I pick for my capstone?
Pick deep learning when your inputs are unstructured (images, audio, video, text) and you have 10,000+ training samples. Pick classical ML (random forest, SVM, logistic regression) for tabular data, small datasets (under 1,000 rows), or when you need explainable predictions for the panel. Many capstones combine both: deep learning for feature extraction (face embedding via FaceNet) plus classical ML on top (SVM classifier for identity matching).
Why is my OpenCV deep learning model running at 2 FPS?
Three usual causes: (1) Resolution too high, resize frames to 640x480 or 320x240 before inference. (2) Wrong cv2.dnn backend, set net.setPreferableBackend(cv2.dnn.DNN_BACKEND_OPENCV) and net.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU). (3) Heavy model on weak hardware, swap YOLO v5 for MobileNet-SSD or use Haar cascades for simple face/eye detection. Also close other applications and disable laptop battery-save throttling.
Can I extend a single OpenCV demo into a full BSIT capstone?
Yes, and you should. A standalone webcam demo (face detection alone) is too narrow for capstone scope. Wrap it in a real system: face recognition becomes Real-Time Attendance System with PHP/MySQL dashboard, object detection becomes Smart CCTV Alert System with email notifications, drowsiness detection becomes Driver Monitoring System for fleet vehicles. Add user accounts, database logging, simple admin UI, and write Chapters 1-5 manuscript to satisfy panel requirements.
How often is this deep learning projects list updated?
New deep learning projects are added periodically as we receive student requests and new models become OpenCV-compatible. Last refreshed June 2026 with 19 vision-focused projects covering face recognition, object detection, traffic-sign classification, OCR, and more.