Create Custom Deep Learning Models with Teachable Machine

Table of Contents

1. Introduction
2. What is Teachable Machine?
3. Training an Image Classification Model with Teachable Machine
   1. Step 1: Go to the Teachable Machine website
   2. Step 2: Choose the type of image model
   3. Step 3: Uploading the training data
   4. Step 4: Setting the training parameters
   5. Step 5: Training the model
4. testing the Trained Model
   1. Testing with the TensorFlow JavaScript code
   2. Downloading the trained model
   3. Testing with Keras and Saved Model conversion types
   4. Deploying the model on mobile devices with TensorFlow Lite
5. Conclusion

Introduction

In this video Tutorial, I will show you how to train an image classification model using Teachable Machine. Teachable Machine is a platform provided by Google that allows you to train deep learning models for Image Recognition tasks. The objective of this tutorial is to train a custom object image classification model using Teachable Machine, test it online, and export it into different formats for use in your projects.

What is Teachable Machine?

Teachable Machine is a training platform developed by Google for creating custom image classifiers. It supports various model formats, such as TensorFlow, TensorFlow Lite, and TensorFlow.js. With Teachable Machine, you can easily train models using your own datasets and then export them for deployment on different platforms.

Training an Image Classification Model with Teachable Machine

Step 1: Go to the Teachable Machine website

To start training an image classification model, visit the Teachable Machine website. The website provides an overview of the platform and offers tutorial videos to help you get started.

Step 2: Choose the type of image model

Teachable Machine offers two options for image models: the standard image model and the embedded image model. The standard image model is suitable for most users and supports 224x224 pixel color images. The embedded image model, on the other HAND, is optimized for microcontrollers and supports 96x96 pixel grayscale images. Choose the model that best fits your requirements.

Step 3: Uploading the training data

To train your custom image classifier, you need to provide training data. You can either capture images using your webcam or upload an existing dataset. Teachable Machine allows you to upload multiple images for each class. Once you have uploaded the training data, you can review and edit the class labels.

Step 4: Setting the training parameters

Before starting the training process, you can customize the training parameters such as the number of epochs, batch size, and learning rate. Teachable Machine provides default values for these parameters, but you can adjust them according to your needs.

Step 5: Training the model

Once you have set the training parameters, you can start the training process. Teachable Machine will display the progress of the training, including the accuracy plots for each class and the loss for each epoch. The training process usually completes quickly for image classification models.

Testing the Trained Model

After training the model, it is important to test its performance. Teachable Machine provides a preview window where you can test the model with custom images or your webcam. You can also download the trained model in different formats, such as TensorFlow, Keras, and TensorFlow Lite, to test it locally on your system.

Testing with the TensorFlow JavaScript code

Teachable Machine offers a code snippet for testing the trained model with TensorFlow.js. You can copy this code snippet and run it on your system to see the model's predictions. The code snippet includes the URL for your trained model, so make sure to update it accordingly.

Downloading the trained model

Teachable Machine allows you to download the trained model in different formats, such as Keras and Saved Model. These formats are suitable for testing the model locally on your system using Python scripts. You can download the model and run it using the provided code snippets.

Deploying the model on mobile devices with TensorFlow Lite

If you want to deploy the image classification model on mobile devices, Teachable Machine also supports TensorFlow Lite. TensorFlow Lite allows you to optimize the model for mobile deployment and run it on devices with limited resources. Teachable Machine provides code snippets and instructions for deploying the model on Android devices.

Conclusion

Training an image classification model using Teachable Machine is a straightforward process. The platform provides an intuitive interface for uploading training data, setting training parameters, and monitoring the training progress. With Teachable Machine, you can quickly train, test, and export custom image classifiers for various applications.

🌟 Highlights

• Train an image classification model using Teachable Machine
• Choose between the standard image model and embedded image model
• Upload training data and customize training parameters
• Test the trained model with custom images or webcam
• Download the model for TensorFlow, Keras, and TensorFlow Lite
• Deploy the model on mobile devices with TensorFlow Lite

FAQ

Q: Can I train multiple image classification models using Teachable Machine? A: Teachable Machine currently supports training one model at a time. If you need to train multiple models, you can utilize platforms like Google Colab, which provide more flexibility.

Q: Is Teachable Machine suitable for training object detection models? A: No, Teachable Machine is specifically designed for image classification tasks. For object detection models, you would need to use other platforms or frameworks.

Q: Can I fine-tune pre-trained models using Teachable Machine? A: Teachable Machine does not support fine-tuning of pre-trained models. It focuses on training custom image classifiers using your own datasets.

Q: Can I use Teachable Machine for other machine learning tasks besides image classification? A: As of now, Teachable Machine is primarily geared towards image classification. However, the development team is working on expanding its capabilities to include other machine learning tasks in the future.

Q: Can I use Teachable Machine on platforms other than Google Chrome? A: Teachable Machine is compatible with most modern web browsers. However, for the best experience, it is recommended to use Google Chrome.

Q: Are there any limitations to the number of images or classes I can use in Teachable Machine? A: Teachable Machine does not have strict limitations on the number of images or classes. However, keep in mind that larger datasets might require more computational resources and training time.