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Course overview
AI object detection refers to a process where artificial intelligence is used to identify and classify objects within images or videos. This technology relies on machine learning and computer vision techniques to analyze visual data, recognizing objects and assigning them labels. Widely applied in industries such as security, retail, healthcare, and autonomous driving, AI object detection uses models like YOLO (You Only Look Once), SSD (Single Shot Multibox Detector), and Faster R-CNN to process images in real-time or from datasets. The goal is to enhance automation, improve accuracy in object recognition, and provide efficient data analysis.
What will i learn?
- Understanding of Key Concepts: Learners will grasp foundational concepts like bounding boxes, classification, and confidence scores used in AI object detection.
- Problem-Solving with AI: Students will develop critical thinking and problem-solving skills needed to address challenges in AI projects, such as managing large datasets and improving model accuracy.
- Career-Ready Knowledge: Completing the course prepares learners for roles in AI development, data science, and machine learning engineering, making them competitive in the job market.
Requirements
- html
- javascript
- tenserflow
Curriculum for this course
5 Lessons
03:50:00 Hours
Object Detection
2 Lessons
03:50:00 Hours
-
Introduction
Preview 03:50:00
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Object Detection
Preview .
Vision resource
1 Lessons
00:00:00 Hours
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Create a Custom Vision project
.
Training Model
1 Lessons
00:00:00 Hours
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Train and test a model
.
Object detection model
1 Lessons
00:00:00 Hours
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Publish and consume the object detection model
.
Frequently asked question
What is AI object detection?
AI object detection is a technology that uses artificial intelligence to identify and classify objects within images or videos, often in real-time, using machine learning and computer vision techniques.
How does AI object detection work?
It works by analyzing visual data, such as images or video frames, and using algorithms to detect objects. Models like YOLO, SSD, and Faster R-CNN are commonly used to process and classify these objects.
What are the applications of AI object detection?
AI object detection is used in various fields, including security (for surveillance), healthcare (to analyze medical images), retail (for inventory management), and autonomous driving (to detect pedestrians and other vehicles).
What are the popular algorithms used in AI object detection?
Some of the popular algorithms include YOLO (You Only Look Once), SSD (Single Shot Multibox Detector), and Faster R-CNN, each offering different trade-offs between speed and accuracy.
What is the difference between object detection and object recognition?
Object detection identifies and localizes objects within an image, while object recognition only classifies objects without determining their location.
What datasets are used for training AI object detection models?
Commonly used datasets include COCO (Common Objects in Context), PASCAL VOC, and ImageNet, which contain labeled images used to train and evaluate object detection models.
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