YOLO v4 and TF 2.0

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

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Description

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.

  1. Anaconda installation.

  2. Image dataset resizing.

  3. Image dataset labeling.

  4. YOLO to PASCAL VOC conversion for TF2.0 training.

  5. YOLOv4 training and tflite conversion on Google Colab.

  6. YOLOv4 Android deployment.

  7. SSD Mobilenet TF2.0 training and tflite conversion on Google Colab.

  8. SSD Mobilenet Android deployment.

  9. YOLOv4 and SSD technical details. Which include

    Basics

    1. Precision  and Recall

    2. IoU(Intersection Over Union)

    3. Mean Average Precision/Average Precision(mAP/AP)

    4. Batch Normalization

    5. Residual blocks

    6. Activation function

    7. Max pooling

    8. Feature Pyramid Networks(FPN)

    9. Path Aggregation Network (PAN)

    10. SPP (spatial pyramid pooling layer)

    11. Channel Attention Module(CAM) and Spatial Attention Module (SAM)

    YOLOv4 - Technical details

    1. Backbone

    2. Cross-Stage-Partial-connections (CSP)

    3. YOLO with SPP

    4. PAN in YOLOv4

    5. Spatial Attention Module (SAM) in YOLOv4

    6. Bag of freebies (Bof) and Bag of specials (BoS)

    SSD - Technical details

    1. Architecture overview and working

    2. Loss functions

    YOLO vs SSD

    1. Speed and accuracy benchmarking

What You Will Learn!

  • Training YOLOv4 and TensorFlow 2.0 custom object detector on Google Colab with one-time labeling
  • Deploying the trained models as an android app with real-time inference
  • Image annotation and conversion of YOLO format to PASCAL VOC format
  • Technical details of YOLOv4 and SSD Mobilenet with benchmarking

Who Should Attend!

  • Python developers who wish to train and deploy their state of the art object detection models
  • Developers who wish to have hands-on experience in the training pipeline for object detection
  • Students who wish to understand the technical details regarding YOLOv4 and SSD