Duration: 5 days – 35 hrs
Overview
The Image Recognition and AI Fundamentals Training Course is designed to provide participants with a comprehensive understanding of image recognition techniques and the fundamentals of artificial intelligence (AI). The course explores the principles and applications of image recognition in various industries, focusing on the use of deep learning and convolutional neural networks (CNNs) for accurate image analysis.
Throughout the course, participants will gain hands-on experience in building and training CNN models for image recognition tasks. They will learn about image preprocessing techniques, including resizing, cropping, normalization, and noise reduction, to improve the quality of input images. Participants will also delve into advanced topics such as optical character recognition (OCR) for vehicle plate number recognition and attribute recognition for vehicles.
Objectives
- Understand the basic concepts and principles of image recognition and its applications in diverse industries.
- Gain knowledge of the fundamentals of artificial intelligence and machine learning, with a focus on deep learning techniques.
- Explore the architecture and components of convolutional neural networks (CNNs) used in image recognition.
- Develop proficiency in preprocessing techniques to enhance image quality and reduce noise.
- Acquire practical skills in building, training, and evaluating CNN models for image recognition tasks.
- Learn optical character recognition (OCR) techniques for vehicle plate number recognition.
- Gain insights into attribute recognition for vehicles, such as the number of wheels and vehicle type (light, medium, heavy).
- Understand the importance of transfer learning and pre-trained models in image recognition.
- Apply learned concepts to real-world scenarios through hands-on exercises and projects.
- Evaluate the performance of image recognition systems and make improvements based on results.
Audience
- Data scientists and machine learning engineers interested in image recognition and AI.
- Software developers seeking to expand their knowledge of deep learning and image analysis.
- Professionals from industries such as automotive, security, healthcare, and retail, where image recognition plays a crucial role.
- Researchers and enthusiasts looking to explore the possibilities of AI in image recognition
Pre- requisites
- Basic understanding of Python programming.
- Familiarity with fundamental concepts of machine learning and neural networks would be beneficial but not mandatory.
Course Content
Day 1: Introduction to Image Recognition and AI Fundamentals
- Introduction to image recognition and its applications in various industries
- Overview of artificial intelligence and machine learning concepts
- Understanding different types of image recognition tasks
- Introduction to deep learning and convolutional neural networks (CNNs)
- Hands-on exercise: Setting up the development environment and installing necessary tools and libraries
Day 2: Image Preprocessing Techniques
- Importance of preprocessing in image recognition
- Image resizing, cropping, and normalization techniques
- Applying filters and enhancing image quality
- Handling image noise and artifacts
- Hands-on exercise: Implementing image preprocessing techniques using Python libraries
Day 3: Building Convolutional Neural Networks (CNNs) for Image Recognition
- Understanding the architecture of CNNs
- Convolutional layers, pooling layers, and activation functions
- Training CNN models for image recognition
- Transfer learning and using pre-trained CNN models
- Hands-on exercise: Building and training a CNN model for image recognition using a deep learning framework
Day 4: Vehicle Plate Number Recognition
- Introduction to Optical Character Recognition (OCR)
- Techniques for detecting and extracting vehicle plate numbers
- Text preprocessing and feature extraction for OCR
- Training an OCR model for vehicle plate number recognition
- Hands-on exercise: Implementing vehicle plate number recognition using OCR techniques
Day 5: Vehicle Attribute Recognition
- Detecting vehicles in images using object detection techniques
- Extracting vehicle attributes such as the number of wheels and vehicle type (light, medium, heavy)
- Deep learning-based approaches for attribute recognition
- Evaluating the performance of the image recognition system
- Hands-on exercise: Developing an image recognition system to detect vehicle attributes
Note: Each day will include theoretical concepts, practical exercises, and discussions on real-world applications of image recognition for vehicle-related tasks. The course will provide hands-on experience with popular deep learning frameworks and libraries such as TensorFlow, Keras, or PyTorch. Participants will gain a comprehensive understanding of image recognition techniques and the ability to apply them to solve real-world problems in the field of vehicle analysis and identification.
