Valueerror arrays must all be same length
One of the common error that developers may encounter is the ValueError: Arrays Must All Be Same Length. This error usually occurs when we are trying to perform operations on …
JavaScript error: Ipython is not defined in Jupyter Lab
Are you encountering the JavaScript error: Ipython is not defined error message while working with an application within the browser like Jupyter Lab. And you are confused about how to troubleshoot this …
Referenceerror: __dirname is not defined in es module scope
When transitioning from the CommonJS module system to ES modules, developers may encounter the “ReferenceError: __dirname is not defined in ES module scope” error. Wherein modern JavaScript development, the adoption …
Valueerror empty separator
In Python programming, the ValueError: empty separator is a common error that developers encounter when working with strings. This error occurs when attempting to split a string using a separator …
Uncaught referenceerror is not defined
One of the most common mistakes that developers often come across in JavaScript is the “Uncaught ReferenceError is not defined” error. In this article, we will explore the complexities of …
Exception in nodemon killing node referenceerror: primordials is not defined
Developers working with Node.js, have instances encounter errors like “Exception in Nodemon: Killing Node ReferenceError: Primordials is not Defined“. Which can lead to confusion and impede the development workflow. This …
Valueerror: per-column arrays must each be 1-dimensional
The Valueerror: per-column arrays must each be 1-dimensional error occurs when we attempt to perform an operation that requires one-dimensional arrays, but your input data is not properly formatted. How …
Uncaught syntaxerror unexpected number
The uncaught syntaxerror unexpected number error message typically happens while you are working with JavaScript. If you’re wondering and perplexed why it occurs and how to troubleshoot this error message, …
Valueerror no gradients provided for any variable
The error message “ValueError: No gradients provided for any variable” typically occurs in machine learning frameworks, such as TensorFlow or PyTorch, when trying to compute gradients during the training process. …
mixing dicts with non-series may lead to ambiguous ordering.
This ValueError: Mixing Dicts with Non-Series May Lead to Ambiguous Ordering error occurs when we attempt to combine dictionaries with non-series objects in a way that leads to an ambiguous …
[Fixed] SyntaxError: Expected ‘except’ or ‘finally’ Block in Python — 3 Solutions
Today, we are going to deal with the Python syntaxerror expected ‘except’ or ‘finally’ block error message. If this error keeps on bothering you, then continue reading. This article offers comprehensive explanations, …
Valueerror assignment destination is read-only
One of the errors that developers often come across is the ValueError: assignment destination is read-only. This error typically occurs when you try to modify a read-only object or variable. …
Referenceerror request is not defined
In this article, we will delve into the ReferenceError with a specific focus on the error message “ReferenceError: Request is not defined.” We will explore its definition, common causes, and …
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.