Description
Deep Learning for Object Detection with Python and PyTorch course. Deep learning course for object recognition with Python and PyTorch. Are you ready to enter the wonderful world of object recognition using deep learning? In our comprehensive course “Deep Learning for Object Detection with Python and PyTorch”, we guide you through the essential concepts and techniques needed to identify, classify, and locate objects in images. Object detection has a wide range of potential real-world applications in many fields. Object recognition is used for self-driving vehicles to perceive and understand their surroundings. It helps to identify and track pedestrians, vehicles, traffic signs, traffic lights and other objects on the road. Object Detection is used for surveillance and security using drones to identify and track suspicious activity, intruders and objects of interest. Object detection is used for traffic monitoring, helmet and license plate recognition, player tracking, defect detection, industrial use, and more. With the powerful combination of Python programming and the PyTorch deep learning framework, you will explore advanced algorithms and architectures such as R-CNN, Fast RCNN, and Faster R-CNN. During the course, you will gain a thorough understanding of Convolutional Neural Networks (CNN) and their role in object recognition. You will learn how to use pre-trained models, tune them to detect objects using the Detectron2 library developed by Facebook AI Research (FAIR). This course covers the complete pipeline with hands-on experience of object recognition using deep learning with Python and PyTorch as follows:
- Learning to recognize objects with Python and Python coding
- Learning object recognition using deep learning models
- Introduction to Convolutional Neural Networks (CNN)
- Learn RCNN, Fast RCNN, Faster RCNN and Mask RCNN Architectures
- Perform object detection with Fast RCNN and Faster RCNN
- Introducing Detectron2 by Facebook AI Research (FAIR)
- Object detection prototype with Detectron2 models
- Explore custom object detection datasets with annotations
- Perform object detection on custom datasets using deep learning
- Training, testing, evaluating your object recognition models and visualizing the results
- Perform object sample segmentation at the pixel level using Mask RCNN
- Perform object instance partitioning on custom datasets with Pytorch and Python
At the end of this course, you will have the knowledge and skills needed to start applying Deep Learning in object recognition in your work or research. Whether you’re a computer vision engineer, data scientist, or developer, this course is a great way to take your understanding of deep learning to the next level. Let’s start this exciting journey of deep learning for object recognition with Python and PyTorch.
What you will learn in Deep Learning for Object Detection with Python and PyTorch course
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Learning to recognize objects with Python and Python coding
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Learning object recognition using deep learning models
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Introduction to Convolutional Neural Networks (CNN)
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Learn RCNN, Fast RCNN, Faster RCNN and Mask RCNN Architectures
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Perform object detection with Fast RCNN and Faster RCNN
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Introducing Detectron2 by Facebook AI Research (FAIR)
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Object detection prototype with Detectron2 models
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Explore custom object detection datasets with annotations
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Perform object detection on custom datasets using deep learning
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Training, testing, evaluating your object recognition models and visualizing the results
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Perform object sample segmentation at the pixel level using Mask RCNN
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Perform object instance partitioning on custom datasets with Pytorch and Python
This course is suitable for people who
- This course is designed for a wide range of students and professionals, including, but not limited to: machine learning engineers, deep learning engineers, data scientists, computer vision engineers, and researchers who want to learn how to use PyTorch to build and Use deep learning training. Models for object recognition
- Overall, this course is for those who want to learn how to use deep learning to extract meaning from visual data and gain a deeper understanding of the theory and practical applications of object detection using Python and PyTorch.
Course specifications Deep Learning for Object Detection with Python and PyTorch
Course headings Deep Learning for Object Detection with Python and PyTorch
Deep Learning for Object Detection with Python and PyTorch course prerequisites
- Object Detection using Deep Learning with Python and PyTorch is taught in this course by following a complete pipeline from Zero to Hero
- No prior knowledge of Semantic Segmentation is assumed. Everything will be covered with hands-on trainings
- A Google Gmail account is required to get started with Google Colab to write Python Code
Course images
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