Learn Complete Deep Learning Projects In Python From Scratch
What you will learn
Explore the process of collecting and preprocessing datasets of facial expressions, ensuring the data is optimized for training a YOLOv7 model.
Dive into the annotation process, marking facial expressions on images to train the YOLOv7 model for accurate and robust emotion detection.
Explore the end-to-end training workflow of YOLOv7 using the annotated and preprocessed dataset, adjusting parameters and monitoring model performance.
Understand how to deploy the trained YOLOv7 model for real-world emotion detection tasks, making it ready for integration into applications or systems.
Description
Course Title: Learn Complete Deep Learning Projects In Python From Scratch
Course Description:
Welcome to the comprehensive course on “Learn Complete Deep Learning Projects In Python From Scratch using Roboflow.” This course is designed to provide students, developers, and healthcare enthusiasts with hands-on experience in implementing the YOLOv8 object detection algorithm for the critical task of detecting brain tumors in MRI images. Through a complete project workflow, you will learn the essential steps from data preprocessing to model deployment, leveraging the capabilities of Roboflow for efficient dataset management.
What You Will Learn:
- Introduction to Medical Imaging and Object Detection:
- Gain insights into the crucial role of medical imaging, specifically MRI, in detecting brain tumors. Understand the fundamentals of object detection and its application in healthcare using YOLOv8.
- Setting Up the Project Environment:
- Learn how to set up the project environment, including the installation of necessary tools and libraries for implementing YOLOv8 for brain tumor detection.
- Data Collection and Preprocessing:
- Explore the process of collecting and preprocessing MRI images, ensuring the dataset is optimized for training a YOLOv8 model.
- Annotation of MRI Images:
- Dive into the annotation process, marking regions of interest (ROIs) on MRI images to train the YOLOv8 model for accurate and precise detection of brain tumors.
- Integration with Roboflow:
- Understand how to seamlessly integrate Roboflow into the project workflow, leveraging its features for efficient dataset management, augmentation, and optimization.
- Training YOLOv8 Model:
- Explore the complete training workflow of YOLOv8 using the annotated and preprocessed MRI dataset, understanding parameters, and monitoring model performance.
- Model Evaluation and Fine-Tuning:
- Learn techniques for evaluating the trained model, fine-tuning parameters for optimal performance, and ensuring accurate detection of brain tumors in MRI images.
- Deployment of the Model:
- Understand how to deploy the trained YOLOv8 model for real-world brain tumor detection tasks, making it ready for integration into a medical environment.
English
language
Content
Introduction To Complete Deep Learning Projects In Python From Scratch
Introduction To Brain Tumor Detection Using YOLOv8 Project
PROJECT CREATION
DATASET CREATION FOR BRAIN TUMOR DETECTION
ANNOTATION FOR DATASET
TRAINING DATASET WITH YOLOV8 MODEL
VALIDATE MODEL
PROJECT EXECUTE IN PYCHARM IDE
INTRODUCTION TO EMOTION DETECTION USING YOLOv7 PROJECT
INTRO TO PROJECT
ACCOUNT CREATION
DATASET CREATION
ANNOTATION AND LABELLING
TRAIN YOLOV7 MODEL
VALIDATE YOLOV7 MODEL
PROJECT EXECUTION IN PYCHARM
INTRODUCTION TO FACE RECOGNITION USING YOLOv7 PROJECT
INTRO TO PROJECT
PROJECT CREATION
DATASET CREATION USING VIDEOS AND IMAGES
ANNOTATION FOR DATASET
TRAIN YOLOV7 MODEL
VALIDATE YOLOV7 MODEL
PROJECT EXECUTE IN PYCHARM
INTRODUCTION TO GOOGLE COLAB
INTRO TO COLAB
IMPORT PROJECT
TRAIN MODEL IN COLAB
VALIADATE MODEL IN COLAB
DOWNLOAD MODEL IN COLAB