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Object Detection with Tensorflow | Practical oriented with real time dataset form Scratch

person icon Aakash Jadhav

4.4

Object Detection with Tensorflow | Practical oriented with real time dataset form Scratch

From Beginner Level to Advanced Level | Completely Practical Oriented | 100% NO Coding background is required to learn this course.

updated on icon Updated on May, 2024

language icon Language - English

person icon Aakash Jadhav

English [CC]

category icon Development,TensorFlow

Lectures -10

Resources -4

Duration -2.5 hours

4.4

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Course Description

Machine learning is an exponential technology from IBM Watson, SIRI, self driving car which are taking on the world and artificial neural network is heart of it. Object Detection is an essential technique for artificial visualization in Robotics, AI Sensory tools and many more. But the problem is Object detection with your own dataset is simply not taught at university.

So where do you learn them?

Well, online training is good option, and that's why we had a look at some of the Tensorflow object detection courses available online right now and what we found was that you all have solid background in computer science or mathematics to understand what's going on and none of the course was robust in structure. That's why we developed fully practical oriented course where non technical person can also train the tensorflow model. With your own dataset, This course will help you to detect your own object from the surroundings.

Watch this excellent preview and Demo video of the course using below link

Click Here to Watch Demo !

The first thing I have focused on is a robust structure navigating a complex topic with training your own dataset. This course won't required any coding background. All types of students can learn this course. Anyone can change their existing domain with computer vision.

The course is broken down into practical sections like,

  1. Tensorflow introduction to latest framework 2021
  2. Tensorflow CPU installation with anaconda
  3. Tensorflow GPU installation with NVIDIA Toolkit and CUDNN library (optional)
  4. Dataset preparation using Kaggle's dataset or custom dataset
  5. Image annotation to perform faster RCNN algorithm
  6. Conversion to TFRECORD for input pipeline
  7. Training your own model with tensor board visualization
  8. Deployment of your trained model on Android application, web application and Realtime application

At the end of the course students will be able to detect any object from surroundings i.e. Dataset preparation to Training you own model. We are extremely passionate about bringing Machine learning to everybody on this planet and today we need your support to make this dream in reality.

Start a career path in Machine learning and enroll today. I will see you inside the course.

Goals

What will you learn in this course:

  • Learn Python / Anaconda Usage.
  • Learn to Prepare Opensource and Custom Dataset.
  • Learn to install Tensorflow Framework.
  • Learn Image annotation and Image Labelling.
  • Learn to Train the ML Model.
  • Learn to Visualize the trained model on Tensorboard - Graphs, Histogram.
  • Learn to Deploy model on Android sample, Webserver, Webcam and Video analysis.

Prerequisites

What are the prerequisites for this course?

  • Laptop or Desktop
  • NVIDIA Graphic Card (Optional)
  • No programming experience needed. You will learn everything that you need to know.
  • This course is for all students (Technical and Non Technical).
  • Spoon feeding course.
Object Detection with Tensorflow | Practical oriented with real time dataset form Scratch

Curriculum

Check out the detailed breakdown of what’s inside the course

Course Preview
1 Lectures
  • play icon Course Demo 02:16 02:16
Introduction to the roadmap of course
1 Lectures
Tutorialspoint
Tensorflow Installation
2 Lectures
Tutorialspoint
Tensorflow Framework Installation
1 Lectures
Tutorialspoint
Dataset Preparation, Labelling / Annotations & Dataset Conversion
2 Lectures
Tutorialspoint
Training and Deployment (Webserver, Webcam, Video Feed, Android)
3 Lectures
Tutorialspoint

Instructor Details

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Aakash Jadhav

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