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YOLO v4 and TF 2.0

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YOLO v4 and TF 2.0, available at $19.99, has an average rating of 3.5, with 10 lectures, based on 20 reviews, and has 150 subscribers.

You will learn about 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 This course is ideal for individuals who are Python developers who wish to train and deploy their state of the art object detection models or Developers who wish to have hands-on experience in the training pipeline for object detection or Students who wish to understand the technical details regarding YOLOv4 and SSD It is particularly useful for Python developers who wish to train and deploy their state of the art object detection models or Developers who wish to have hands-on experience in the training pipeline for object detection or Students who wish to understand the technical details regarding YOLOv4 and SSD.

Enroll now: YOLO v4 and TF 2.0

Summary

Title: YOLO v4 and TF 2.0

Price: $19.99

Average Rating: 3.5

Number of Lectures: 10

Number of Published Lectures: 10

Number of Curriculum Items: 10

Number of Published Curriculum Objects: 10

Original Price: ₹1,199

Quality Status: approved

Status: Live

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

Target Audiences

  • 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

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 YOLOv4and TensorFlow 2.0and 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

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Anaconda Python installation

Lecture 1: Anaconda Python installation

Chapter 3: Imge resizing for training

Lecture 1: Dataset preperation and image resizing

Chapter 4: image annotation

Lecture 1: Image labeling

Chapter 5: YOLO to PASCAL VOC

Lecture 1: YOLO format to PASCAL VOC format

Chapter 6: YOLOv4 training

Lecture 1: YOLOv4 training on Google Colab

Chapter 7: YOLOv4 android deployment

Lecture 1: YOLOv4 android deployment for real-time inference

Chapter 8: SSD Mobilenet TF2.0 training

Lecture 1: SSD Mobilenet training on Google Colab and tflite conversion

Chapter 9: SSD Mobilenet android deployment

Lecture 1: Android deployment

Chapter 10: Technical details

Lecture 1: YOLOv4 and SSD – Technical details and benchmarking

Instructors

Rating Distribution

  • 1 stars: 3 votes
  • 2 stars: 1 votes
  • 3 stars: 2 votes
  • 4 stars: 4 votes
  • 5 stars: 10 votes

Frequently Asked Questions


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Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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