Master object detection fundamentals and advanced techniques through this comprehensive 13-hour tutorial covering multiple state-of-the-art algorithms and practical implementations. Explore the core concepts of object detection, including its various modes, types, and critical importance in computer vision applications. Learn to implement YOLO variants from scratch, starting with YOLOv3 network architecture and progressing through YOLOv4, YOLOv5, YOLOv6, YOLOv7, and YOLOv8 for both standard and custom datasets. Dive into specialized applications including traffic sign detection, leaf disease detection, and fall detection using custom-trained models. Discover object tracking capabilities by combining DeepSORT with YOLOv5 for multi-object tracking scenarios. Master alternative detection frameworks including Faster R-CNN with region proposal networks and ROI pooling, Single Shot Detector (SSD) architecture, and Mask R-CNN for instance segmentation tasks. Gain hands-on experience with mobile deployment through TensorFlow Lite Android app development and explore cutting-edge techniques like 3D object detection using LiDAR datasets. Learn to work with advanced models including YOLOR, YOLO-NAS, and integration with Segment Anything Model (SAM) for enhanced segmentation capabilities. Practice implementing custom object detectors for real-world applications such as plant disease detection and develop skills in both detection and tracking using YOLO-NAS with StrongSORT algorithms.

Syllabus

YOLO - Object Detection Using Python
Object Detection Using Yolo | Yolo V 3 network from scratch using Python
YOLOv4 | Object Detection Using Yolo v4
Object Detection Using YOLO v4 on Custom Dataset | Practical Implementation
Install Darknet framework | Object Detection using yolov4
Traffic Sign Detection Using Yolo V4 | Yolov4 on Custom Dataset
Yolo v5 on Custom Dataset | Train and Test Yolov5 on Custom Dataset
Leaf Disease Detection using yolov5 | Object Detection
Object Tracking Using DeepSORT and YOLOv5 | Multi Object Tracking
Object Detection Using YOLOv4-tiny | Part 1
TFLite Object Detection Android App Tutorial | Object Detection Using Yolov4 tiny
YOLOR on a Custom Dataset | Object detection using YOLOR
YOLOv6 on Custom dataset | Object Detection
YOLOv6 on Custom Dataset | Object Detection
YOLOv7 | Object Detection | instance segmentation | keypoints detection
YOLOv7 | Object Detection on Custom Dataset
YOLOv7 | Instance Segmentation on Custom Dataset
Official YOLOv7 | Object Detection
1 Object Detection Using Faster R-CNN
2 Faster R-CNN | Object Detection Using Faster R-CNN
3 Region Proposal Network | Faster R-CNN
4 Region Of Interest (RoI Pooling) | Faster R-CNN | Object Detection Using Faster R-CNN
Faster R-CNN on Custom Dataset | Custom Object Detector
Mask R-CNN
Mask R-CNN on Custom Dataset | Practical Implementation
Mask R-CNN | Practical Implementation
Mask R-CNN Practical Implementation
Plant leaf disease detection using Mask R-CNN | Image Segmentation
Single Shot Detector | SSD | Object Detection Using SSD
L-1 Single Shot Detector Practical Implementation
L-3 Single Shot Detector | Model Testing
L-2 Single Shot Detector | Code for SSD Model
Single Shot Detector On Custom Dataset | SSD | Object Detection Using SSD
Single Shot Detector On Custom Dataset | Google Colab Implementation | Object Detection
Single Shot Detector On Custom Dataset | Test your Custom SSD Model
3D Object Detection using YOLO4 | LiDAR Dataset
YOLOv8 | Object Detection on a Custom Dataset using YOLOv8
YOLOv8 | Image Segmentation On Custom Dataset using YOLOv8
YOLO-NAS Custom Object Detection | Fall Detection Using YOLO-NAS
YOLO-NAS + SAM : Image Segmentation Using YOLO-NAS and Segment Anything Model
YOLO-NAS and StrongSORT | Object detection and tracking