This course includes:
Welcome to my second course on computer vision. In this course, you will understand the two most latest State Of The Art(SOTA) object detection architecture, which is YOLOv4 and TensorFlow 2.0 and its training pipeline. I also included a one-time labeling strategy, so that you won't have to re-label the image for TensorFlow training. The course is split into 9 parts.
Image dataset resizing.
Image dataset labeling.
YOLO to PASCAL VOC conversion for TF2.0 training.
YOLOv4 training and tflite conversion on Google Colab.
YOLOv4 Android deployment.
SSD Mobilenet TF2.0 training and tflite conversion on Google Colab.
SSD Mobilenet Android deployment.
YOLOv4 and SSD technical details. Which include
Precision and Recall
IoU(Intersection Over Union)
Mean Average Precision/Average Precision(mAP/AP)
Feature Pyramid Networks(FPN)
Path Aggregation Network (PAN)
SPP (spatial pyramid pooling layer)
Channel Attention Module(CAM) and Spatial Attention Module (SAM)
YOLOv4 - Technical details
YOLO with SPP
PAN in YOLOv4
Spatial Attention Module (SAM) in YOLOv4
Bag of freebies (Bof) and Bag of specials (BoS)
SSD - Technical details
Architecture overview and working
YOLO vs SSD
Speed and accuracy benchmarking
Who this course is for:
10 sections - 10 lectures - 01:22:47 total length
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Data scientist currently working in Deep learning. Developing AI-based robots and android apps for edge and cloud-based applications. Training students to understand concepts in CNN, image processing, computer vision, TensorFlow, YOLO and helping them in rapid prototyping projects. Running a small start-up with a mission to make a happy world