Deep Learning with Domain Randomization Python
Learn how to train any robot to recognize an object and pinpoint its 3D location with only an RGB camera and a lot of training with Keras.
Welcome to this micro-course! This course is intended for the people that want to learn about deep learning using Keras.
In this case, we use a very interesting approach to learning, which is Environment Randomization. This method exploits the versatility of environment generation in simulations to train a robot in a way that the resulting model is very robust, no matter the lighting conditions. It also makes the transition from simulated learning to reality much smoother and faster. Learn through hands-on experience how to train a robot for 3D object recognition using random environments.
Keras will be the cornerstone of this system and you will learn all the necessary skills to generate training data, convert it to a database, train a MobileNetV2 model, retrain it, and make predictions with it.
The final project is the training of a garbage picking robot, from training data generation to the final garbage detection and picking program. Dive into the fantastic world of Deep Learning with Keras right now!
- Use Keras in a basic way
- Train a deep neural network using a Gazebo Simulation
- Work with ROS + Gazebo + Keras in tandem.
- Understand how the Random Environment generation works in Gazebo
Simulation robots used in this course
Fetch Robot, GarbageCollector Robot.
What projects will you be doing?
Create a Simple Random Environment
Start with a simple environment training
Exercises For XY motion Spam
Train the simulation to find the Spam anywhere on the table
Exercises for a Distractor & Random Environment
Train the model with a random environment
Microproject Garbage Collector
Use the two-wheeled Magician robot to find an object amidst other distractions
What you will learn
Unit 1: Introduction
Random Environment: Quick Demo.
Unit 2: Step By Step Simple Guide
We will start with a simple environment training. We will talk about all the steps from training image generation to validation.
Unit 3: Exercises For XY Motion Spam
Here we indicate how to retrain a previously trained model and work on improving our detection model.
Unit 4: Exercises for Spam and a Distractor
Here we are trying to distract the model with another object in the scene that moves around and could be mistaken by the Spam object.
Unit 5: Exercises with Distractor and Random Env
You are going to train the model with a random environment to make it more robust in any lighting condition. You will also train with a huge image database to emulate the real deal.
Unit 6: Microproject Garbage Collector
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