
Explore real-time object detection in video using YOLO on iPhone, learn how to detect cars, pedestrians, traffic lights with low latency for self-driving style applications.
learners explore Evergreen Technologies' online courses in computer vision and natural language processing, with practical image processing techniques, edge detection, and open source Python notebooks for hands-on practice.
Explore the differences between artificial intelligence, machine learning, and deep learning, and how ml uses labeled data while dl learns features automatically with neural networks.
Explain logistic regression as a binary classifier that uses input image pixels and weights to predict cat or dog, and demonstrate weight updates via gradient descent to minimize loss.
Describe how yolo uses a seven by seven grid where each cell predicts bounding boxes and objectness, then combines class probabilities with non maximum suppression to output final detections.
Set up a new Xcode project as a single view app, organize the folder, and add a camera usage description in Info.plist for object detection.
Set up live capture initializes a back camera feed, creates an AVCaptureSession, and configures input and video data output for real-time object detection using a vision-based machine learning model.
Draw real-time bounding boxes around detected objects in video on iPhone, using model observations to generate labels, coordinates, and the confidence rate with dynamic UI updates.
Course Description
Learn to build real time object detection engine using YOLO deep learning algorithm. Deep learning is popular where a machine can be trained to detect objects in video and images. Once trained, it can be used to detect objects in any video or image.
Yolo (You only look Once) algorithm has become popular because of its real time nature. It can detect objects at 45 frames per second or within 20 ms. This makes it attractive to use it in self driving car where detecting objects in real time is key to avoid collisions. Unlike its predecessor, YOLO looks at image only once.
Build a strong foundation in image search engines with this tutorial for beginners.
Understanding fundamentals of YOLO
Understanding fundamentals of deep learning and CNN
Benefits of YOLO for self driving car use case
Build a real life object detection in video using YOLO, coreml and swift
Build a real life object detection in image using YOLO, coreml and swift
A Powerful Skill at Your Fingertips Learning the fundamentals of real time object detection puts a powerful and very useful tool at your fingertips. swift, YOLO and coreml are free, easy to learn, has excellent documentation.
No prior knowledge of CNN or deep learning is assumed. I'll be covering topics like CNN from scratch.
Jobs in object detection area are plentiful, and being able to learn real time object detection will give you a strong edge. YOLO is state of art technology that can quickly help you achieve your goal.
Learning object detection with YOLO will help you become a computer vision developer which is in high demand.
Content and Overview
This course teaches you on how to build real time object detection engine using open source YOLO, OPNCV and Python . You will work along with me step by step to build following answers
Real time object detection in Video
Real time object detection in image
Fundamentals of CNN and YOLO
What am I going to get from this course?
Learn YOLO and build real time object detection engine from professional trainer from your own desk.
Over 10 lectures teaching you how to build real time object detection engine
Suitable for beginner programmers and ideal for users who learn faster when shown.
Visual training method, offering users increased retention and accelerated learning.
Breaks even the most complex applications down into simplistic steps.
Offers challenges to students to enable reinforcement of concepts. Also solutions are described to validate the challenges.