Yolov7 Yolov8 Yolov9 Yolov10 - Deep Learning Course

Train Custom Dataset, Object Detection, Pose Estimation, Instance Segmentation, Image Classification, Cool Web Dashboard

What you'll learn
How to run, from scratch, a YOLOv7, YOLOv8, YOLOv9, & YOLOv10 program to detect 80 object classes in < 10 minutes
How to install and train YOLOv7, YOLOv8, YOLOv9, & YOLOv10 using Custom Dataset and perform Object Detection for image, video and Real-Time using Webcam/Camera
How to use YOLOv7 & YOLOv8 new features: Instance Segmentation, Pose Estimation, Image Classification, Object Tracking + Real-world Projects
6 Real Projects: Masker Detection, Weather Classification, Coffee Leaf Diseases Segmentation, Squat Counter, Various Vehicle Counter Web App, Cattle Counter
YOLOv7, YOLOv8 & YOLOv9 architecture and how it really works
How to find dataset
Data annotation/labeling using LabelImg
Automatic Dataset splitting
How to train YOLO v7, YOLO v8, YOLO v9, and YOLO v10 using custom dataset, transfer learning and resume training.
How to visualize training performance using TensorBoard
Easily understand The Fundametal Theory of Deep Learning and How exactly Convolutional Neural Networks Work
Real-World Project #1: Masker detection using YOLOv7 & YOLOv8
Real-World Project #2: Weather Image/Video Classification using YOLOv8
Real-World Project #3: Coffee Leaf Diseases Segmentation using YOLOv8
Real-World Project #4: Squat Counter based on YOLOv7 Pose Estimation
Real World Project #5: Various Vehicle Counter and Speed Estimation Web App with Cool Dashboard using YOLOv9 + Streamlit
Real World Project #6: Cattle Counter using YOLOv10 + Bytetrack

Requirements
Programming experience is an advantage but not required
Windows laptop/PC, especially with Nvidia GPU

Description
Welcome to the YOLOv7, YOLOv8, YOLOv9, & YOLOv10 Deep Learning Course, a 4 COURSES IN 1. YOLOv7, YOLOv8, YOLOv9, and YOLOv10 are the current four best object detection deep learning models. They are fast and very accurate. YOLOv10 is the latest version of YOLO whereas YOLOv8 is the most popular YOLO version of all.What will you learn:1. How to run, from scratch, a YOLOv7, YOLOv8, YOLOv9, & YOLOv10 program to detect 80 types of objects in < 10 minutes.2. YOLO evolution from YOLO v1 to YOLO v83. What is the real performance comparison, based on our experiment4. What are the advantages of YOLO compares to other deep learning models5. What’s new in YOLOv7 and YOLOv86. How artificial neural networks work (neuron, perceptron, feed-forward network, hidden layers, fully connected layers, etc)7. Different Activation functions and how they work (Sigmoid, tanh, ReLu, Leaky ReLu, Mish, and SiLU)8. How convolutional neural networks work (convolution process, pooling layer, flattening, etc)9. Different computer vision problems (image classification, object localization, object detection, instance segmentation, semantic segmentation)10. YOLOv7, YOLOv8, YOLOv9, and YOLOv10 architecture in detail11. How to find the dataset12. How to perform data annotation using LabelImg13. How to automatically split a dataset14. A detailed step-by-step YOLOv7, YOLOv8, YOLOv9, and YOLOv10 installation15. Train YOLOv7, YOLOv8, YOLOv9, and YOLOv10 on your own custom dataset16. Visualize your training result using Tensorboard17. Test the trained YOLOv7, YOLOv8, YOLOv9, and YOLOv10 models on image, video, and webcam.18. YOLOv7 New Features: Pose Estimation19. YOLOv7 New Features: Instance Segmentation20. YOLOv8 New Features: Instance Segmentation & Object Tracking20. Real World Project #1: Robust mask detector using YOLOv7 and YOLOv821. Real World Project #2: Weather YOLOv8 classification application22. Real World Project #3: Coffee Leaf Diseases Segmentation application23. Real World Project #4: YOLOv7 Squat Counter application24. Real World Project #5: Various Vehicle Counter and Speed Estimation Web App with Cool Dashboard using YOLOv9 + Streamlit 25. Real World Project #6: Cattle Counter using YOLOv10 + Bytetrack

Who this course is for:
Professionals who want to quickly grasp and apply YOLOv7, YOLOv8, YOLOv9, and YOLOv10.,Undergraduate/Graduate students who are taking computer vision using deep learning as their final project,Anyone who is interested in learning Deep Learning and How to Apply it in solving Computer Vision problem