Real World 5+ Deep Learning Projects Complete Course

Learn Real World 5+ Deep Learning Projects Complete Course Using Roboflow and Google Colab

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 performance for bot

Requirements

Access to a computer with internet connectivity.

Basic understanding of machine learning and computer vision concepts.

Description

Course Title: Real World 5+ Deep Learning Projects Complete Course Using Roboflow and Google ColabCourse 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.

Overview

Section 1: Introduction To Real World 5+ Deep Learning Projects Complete Course

Lecture 1 Introduction To Brain Tumor Detection Using YOLOv8 Project

Lecture 2 ROBOFLOW ACCOUNT CREATION

Lecture 3 DATASET CREATION FOR BRAIN TUMOR DETECTION

Lecture 4 ANNOTATION AND LABELLING FOR DATASET

Lecture 5 TRAINING DATASET WITH YOLOv8 MODEL

Lecture 6 VALIDATE TRAINED MODEL

Lecture 7 PROJECT EXECUTION IN PYCHARM IDE

Section 2: INTRODUCTION TO EMOTION DETECTION USING YOLOv7 PROJECT

Lecture 8 INTRO TO COURSE

Lecture 9 EMOTION DETECTION CLASS ONE

Lecture 10 DATASET CREATION USING VIDEOS AND IMAGES

Lecture 11 ANNOTATION FOR DATASET

Lecture 12 TRAIN DATASET WITH YOLOV7 MODEL

Lecture 13 VALIDATE TRAINED MODEL

Lecture 14 PROJECT EXECUTION IN PYCHARM IDE

Section 3: INTRODUCTION TO FACE RECOGNITION USING YOLOv7 PROJECT

Lecture 15 INTRO TO PROJECT

Lecture 16 ACCOUNT CREATION

Lecture 17 DATASET CREATION USING VIDEOS AND IMAGES

Lecture 18 ANNOTATION FOR DATASET

Lecture 19 TRAINING THE DATASET WITH YOLOv7 MODEL

Lecture 20 VALIDATE MODEL IN ROBOFLOW

Lecture 21 PROJECT EXECUTION IN PYCHARM IDE

Section 4: INTRODUCTION TO HELMET DETECTION USING YOLOv7 PROJECT

Lecture 22 INTRO TO PROJECT

Lecture 23 ACCOUNT CREATION

Lecture 24 DATASET CREATION FOR HELMET DETECTION

Lecture 25 ANNOTATION FOR DATASET

Lecture 26 TRAINING YOLOV7 MODEL

Lecture 27 VALIDATE MODEL

Lecture 28 PROJECT EXECUTION IN PYCHARM IDE

Section 5: INTRODUCTION TO GOOGLE COLAB

Lecture 29 INTRO TO COURSE

Lecture 30 IMPORT PROJECT IN GOOGLE COLAB

Lecture 31 TRAINING YOLOV7 MODEL IN COLAB

Lecture 32 VALIDATE MODEL IN COLAB

Lecture 33 DOWNLOAD MODEL IN COLAB

Students and professionals in computer vision, artificial intelligence, or human-computer interaction.,Developers interested in mastering YOLOv7 for multiple computer vision applications.