YOLO v4 and TF 2.0 | Data Science Online Course by Udemy

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Custom object detection training using YOLOv4 and TensorFlow 2.0 with Google Colab and Android deployment

An excellent training about Data Science

YOLO v4 and TF 2.0

Hi everyone, 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. Anaconda installation. 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 includeBasicsPrecision and RecallIoU(Intersection Over Union)Mean Average Precision/Average Precision(mAP/AP)Batch NormalizationResidual blocksActivation functionMax poolingFeature Pyramid Networks(FPN)Path Aggregation Network (PAN)SPP (spatial pyramid pooling layer)Channel Attention Module(CAM) and Spatial Attention Module (SAM) YOLOv4 – Technical detailsBackboneCross-Stage-Partial-connections (CSP)YOLO with SPPPAN in YOLOv4Spatial Attention Module (SAM) in YOLOv4Bag of freebies (Bof) and Bag of specials (BoS)SSD – Technical detailsArchitecture overview and workingLoss functionsYOLO vs SSDSpeed and accuracy benchmarking

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