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This course includes:

  • 1.5 hours on-demand video
  • 11 downloadable resources
  • Full lifetime access
  • Access on mobile and TV

YOLO v4 and TF 2.0

Nandakishor M via Udemy

(4 Reviews)


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


    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

Who this course is for:

  • 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

Course Content

10 sections - 10 lectures - 01:22:47 total length

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Nandakishor M

  • 3 Instructor Rating
  • 23 Reviews
  • 126 Students

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