08-Retrieve and Update Academic Year
This tutorial is about Retrieve and Update Academic Year. This time, I’m going to retrieve and update all the Academic Year in Ms Access Database and there are some changes to …
07-Add Academic Year
Last time we have finished the “Year Level” module of the student. Now, we will add another module which is “Academic Year” in the settings. This module allows you to …
06-Removing Year Level
This tutorial is all about Removing Year Level. Today, I’m going to remove whatever wrong records that have been saved in Ms Access Database. Deleting records in a system is …
05-Retrieve and Update Year Level
This tutorial is all about Retrieve and Update Year Level. This time, i’m going to retrieve the records that have been saved in Ms Access Database. I have made some changes …
04-Add Year Level
This tutorial is all about Add Year Level. In this tutorial I will teach you how to add and manage setting in the Student Information System. The settings are made up …
03-Setting Up Connection Between MS Access Database and Visual Basic 2008
This tutorial is all about Setting Up Connection Between MS Access Database and Visual Basic 2008. Today, i will teach you how to connect Ms Access Database to Visual Basic …
02-Constructing Database
This tutorial is all about Constructing Database. This time, I will create a database in the Microsoft Access. This MS Access is a Database Management System from Microsoft that combines the relational Microsoft Jet …
01-Introduction
This is an Introduction in Student Information System. Today, I’m going to create a Student Information System. This is a step by step tutorial in which you can follow the procedure easily. …
17-Generating Reports (part 3)
This is the continuation of my tutorial about Generating Reports. This time I will show you how to put a Crystal Report in a Windows Form and retrieving the records …
16-Generating Reports (part 2)
In my previous tutorial which is 15-Generating Reports (part 1) I set up the ODBC data source for the Student Registration database. Now, I will create a report of a …
15-Generating Reports (part 1)
This tutorial is all about Generating Reports. In my previous tutorial, I created 14-Removing User/s. Today, I will teach you how to generate a report using a Crystal Report in the …
How to Navigate Records In DataGridView in VB.net And MySQL Database
In this tutorial I will teach you how to navigate records in the DatagridView using Visual Basic 2008 and MySQL Database. This will show you that you can limit your …
How to Fill Data in the ComboBox in VB.net
How to Fill Data in the ComboBox in VB.net This tutorial is all about How to Fill Data in the ComboBox in VB.net Programming language.In this tutorial, I will teach you …
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.