To ease your search for the basic computer vision projects, we’ve made this list. By taking on these projects, you can showcase your skillset by adding them to your current portfolio or resume.
The best beginner computer vision projects are listed here are mini project courses offered in Coursera. The cost of each project is $10, and it provides all the necessary tools in a virtual environment to get started in computer vision projects.
In this article, we’ve curated the best computer vision projects for beginners to get started in computer vision technology. It can also be enrolled by someone who may have recently completed the course in this field.
Skills you develop here are vision tasks, neural network, detection algorithms, face recognition, image classification, image processing, deep learning models, and more.
Why is it Important?
Enroll in these basic computer vision projects that provide you great insight and practical knowledge on the subject. This list also includes intermediate level and advanced level projects for experienced professionals.
The deep practical skills you develop here will be easy to showcase in your resume. These are the things where your recruiters concentrate on and ask you more questions about it. Since you’ll be having practical hands-on knowledge of computer vision, it will be easier to answer and have a higher chance of getting hired.
We recommend you bookmark this page so that you can refer to this page for future reference.
Best Computer Vision Projects For Beginners
1. Computer Vision – Image Basics with OpenCV and Python
If you’re fresher or in a final year of your computer vision course, then this project is the best starting point. This project will train you on computer vision with OpenCV and Python. OpenCV is an open-source computer vision library.
Here you will learn how to open an image using Matplotlib and understand the difference between OpenCV and Matplotlib. This is computer vision projects with python; hence you should have basic knowledge of writing Python codes.
2. AutoML for Computer Vision with Microsoft Custom Vision
To get started in this computer vision projects for the final year, you should have a Microsoft Azure account. If not, you can create one in a few minutes. This is one of the popular projects that use Microsoft’s Custom Vision service for automated machine learning. Also known as AutoML.
In this project, with the help of Microsoft Custom Vision, you will train an image classifier. Finally, using TensorFlow.js you will classify the images in a web browser by creating an HTTP web server. The learning from this project is good, and its a fun computer vision project.
3. Video Basics with OpenCV and Python
Like Image Basics with OpenCV and Python project mentioned which was mentioned earlier, this is with video. In this project, you’ll learn to do with a video. It trains you to record yourself with a computer’s camera, record yourself to draw a line. You can interact on live video and perform face detection.
This project will train you on how to perform face detection using OpenCV and Python. One of the most exciting aspects of using technology to perform visual search and detecting objects using a camera. Over 3.9k students have enrolled in this computer vision projects with python.
4. Analyzing Video with OpenCV and NumPy
Computer Vision is associated with Artificial Intelligence because the computer needs to extract data from images and videos. For the AI to detect an object from an image or video, it is not possible without computer vision technology. The prerequisite of this project is Python programming.
By following the instructions guide from the left side of the screen, you do the tasks on the right side of the screen. By the end, you will have learned to analyze a video, access its content in every frame, and modify it at the pixel level. This is great for beginner computer vision projects.
5. Real-time OCR and Text Detection with Tensorflow, OpenCV and Tesseract
This intermediate computer vision project will teach you to train a TensorFlow convolutional neural network model. This will help to recognize a particular area on an image or frame of video. The project’s length is 2 hours, and more than 1.8k students have taken this simple computer vision project.
The skills that you learn will help in Deep Learning and object detection. Both are essential for further study of Artificial Intelligence. Using OpenCV, you will enhance the image containing text and pass the result to Google’s open-source OCR. The texts are read using pytesseract in Optical Character Recognition software.
6. Computer Vision – Object Detection with OpenCV and Python
The ideas for computer vision projects is to recognize some objects with a camera. This project is exactly what it does. It’s tutorial to learn face detection, detect eyes, and both from a given image and video. More than 7k learners have signed up for this project to acquire this skill.
You are also trained to detect a moving car and to move people in a video. By acquiring such skills, you will have a better understanding of the concepts and technology. In the future, this course will help when you want to learn hand gestures in computer vision.
7. Computer Vision – Object Tracking with OpenCV and Python
Here you learn the working of Optical and Dense Optical Flow. This particular project is track and object in a video using OpenCV and Python. Both OpenCV and Python are essential prerequisites in learning computer vision. Since you get pre-configured desktops in the cloud, there is no need to install additional software or packages.
The course duration is 2 hours long, and some students may find this project at the same level as advanced computer vision projects. But once you enroll and grasp the concept, it will become easier. In the end, you will know how to use MeanShift and CamShist. With this skill, you can track single or multiple objects.
8. Emotion AI: Facial Key-points Detection
This project is perfect for those who want to learn both deep learning and computer vision together. In fact, this is one of the best deep learning computer vision projects on Coursera with guided instructions. You will get a practical understanding of Residual Neural Networks, Deep Neural Networks, and Convolutional Neural Network.
This is achieved as you build and train a deep learning model using Keras with Tensorflow 2.0 as a backend. You also develop confidence when working on deep learning models. This is one of the best computer vision projects for beginners. You can also check out the best deep learning projects for beginners.
9. Computer Vision: Neural Transfer Style & Green Screen Effect
If you’re looking at how Neural Transfer Style works on Images, then this is a perfect project for you. Here you learn how Neural Transfer Style on video and images. It doesn’t matter if its an existing image or live. Then you learn how you can mix two images using Green Screen Effect.
It’s an exciting and fun project in computer vision. The duration of the project is 1 hour. And more than 2k students have taken part in this project.
10. Machine Learning: Create a Neural Network that Predicts whether an Image is a Car or Airplane
The skills you develop in this project are image processing, Machine Learning, Data Analysis, and computer vision. This simple project will show how you can make a prediction based on a given image.
Using Keras and MSIST data set, you start by building a Neural Network Model. Then using One Hot Encoding, you build a classifier and finally evaluate model performance. The result of the project will be to check if the provided image is of a car or an airplane. Some of the best Machine Learning projects for practice are listed here.
11. Facial Expression Recognition with Keras
Face Recognition and Face Detection skillset can further be enhanced to learn the facial expression of a person. In this project, you use OpenCV to recognize a face in an image and draw a box around them. The next step is to classify each faces based on the expression and train the model.
The trained model will then be used in a web interface to perform real-time facial expression recognition on video and image data. All the concepts taught in this project are clear and can be understood by anyone. Even beginners will find this project exciting and easy.
12. Perform Real-Time Object Detection with YOLOv3
This project course on Coursera will teach you to make real-time object detection using YOLOv3. To be precise, the objects are detected with the YOLO system using pre-trained models on GPU enabled computers. The instructor clearly explains every step.
This is one of the best computer vision projects for beginners and intermediate learners. The project tutorial is brief and practical, which uses OpenCV to read the video streams and draw boxes around objects.
13. Generate Synthetic Images with DCGANs in Keras
In this project course, you learn what is Generative Adversarial Networks (GANs) and to build Deep Convolutional GAN with Keras. This project is created to teach you to build and train and Deep Convolutional GAN with Keras. It will help to generate images of fashionable clothes.
At the start, you will learn about GAN functions and how you can use it. The skills you gain by completing this project are TensorFlow, Machine Learning, Keras, and computer vision. Over 4.4k students have enrolled in this advanced computer vision project.
Summary: Computer Vision Mini Projects
These are the course of your step-by-step projects to enhance your skills in computer vision. Advance your career in Computer Vision by enrolling in more than a few projects.
It would help you in the future by bookmarking the best Computer Vision projects for beginners to learn in 2021. Enroll and improve your key skillset in image segmentation, image processing, image classification, hand gesture recognition, object detection algorithms, object tracking algorithm, and more.
* We sometimes use affiliate links in our content, meaning we’ll receive a commission when you buy something via links. This won’t cost you anything but it helps us to offset the costs of our editorial team and keeps this website alive.