Real World 5+ Deep Learning Projects Complete Course
This course includes:
- 2.5 hours on-demand video
- Access on mobile and TV
- Full lifetime access
- Certificate of completion
What you'll learn
- Understand how to integrate Roboflow into the project workflow, leveraging its features for efficient dataset management, augmentation, and optimization for bot
- Explore the process of collecting and preprocessing datasets for both facial recognition and emotion detection, ensuring the data is optimized for training a YO
- Dive into the annotation process, marking facial features on images for recognition and labeling emotions for detection. Train YOLOv7 models for accurate and ro
- Explore the end-to-end training workflow of YOLOv7 using the annotated and preprocessed datasets, adjusting parameters, and monitoring model
Requirements
- Access to a computer with internet connectivity.
- Basic understanding of machine learning and computer vision concepts.
Course Description :
Course Title: Real World 5+ Deep Learning Projects Complete Course Using Roboflow and Google Colab
Course Description:
Welcome to the immersive "Learn Facial Recognition And Emotion Detection Using YOLOv7: Course Using Roboflow and Google Colab." In this comprehensive course, you will embark on a journey to master two cutting-edge applications of computer vision: facial recognition and emotion detection. Utilizing the powerful YOLOv7 algorithm and leveraging the capabilities of Roboflow for efficient dataset management, along with Google Colab for cloud-based model training, you will gain hands-on experience in implementing these technologies in real-world scenarios.
What You Will Learn:
Introduction to Facial Recognition and Emotion Detection:
Understand the significance of facial recognition and emotion detection in computer vision applications and their real-world use cases.
Setting Up the Project Environment:
Learn how to set up the project environment, including the installation of necessary tools and libraries for implementing YOLOv7 for facial recognition and emotion detection.
Data Collection and Preprocessing:
Explore the process of collecting and preprocessing datasets for both facial recognition and emotion detection, ensuring the data is optimized for training a YOLOv7 model.
Annotation of Facial Images and Emotion Labels:
Dive into the annotation process, marking facial features on images for recognition and labeling emotions for detection. Train YOLOv7 models for accurate and robust performance.
Integration with Roboflow:
Understand how to integrate Roboflow into the project workflow, leveraging its features for efficient dataset management, augmentation, and optimization for both facial recognition and emotion detection.
Training YOLOv7 Models:
Explore the end-to-end training workflow of YOLOv7 using the annotated and preprocessed datasets, adjusting parameters, and monitoring model performance for both applications.
Model Evaluation and Fine-Tuning:
Learn techniques for evaluating the trained models, fine-tuning parameters for optimal performance, and ensuring robust facial recognition and emotion detection.
Deployment of the Models:
Understand how to deploy the trained YOLOv7 models for real-world applications, making them ready for integration into diverse scenarios such as security systems or human-computer interaction.
Ethical Considerations in Computer Vision:
Engage in discussions about ethical considerations in computer vision, focusing on privacy, consent, and responsible use of biometric data in facial recognition and emotion detection.
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