In this article, we'll explore how to train an image classification model for mobile apps without writing a single line of code. To achieve this, we'll utilize the Teachable Machine platform and walk you through the entire process of collecting data, training the model, evaluating its performance, and converting it into TensorFlow Lite format.

What is Image Classification?

Image classification is the process of recognizing different entities or things in an image or video. With AI-powered image recognition, you can identify animals, plants, diseases, food, activities, colors, things, fictional characters, drinks, and more.

Training Your Custom Image Classification Model

To train your custom image classification model, you'll need to follow these steps:

  1. Data Collection: Collect at least 50-100 images of each entity or thing you want the model to recognize.
  2. Model Training: Pass the collected dataset to a machine learning algorithm to extract patterns and train the model.
  3. Model Evaluation: Test the trained model by passing new, unseen images through it to evaluate its accuracy.
  4. Model Conversion: Convert the trained model into TensorFlow Lite format for use in mobile apps.

Using Teachable Machine to Train Your Model

Teachable Machine is a powerful tool that allows you to train image classification models without writing any code. To get started:

  1. Click on the "Get Started" button and select the "Image Project" option.
  2. Upload your dataset by creating six classes for each entity or thing in your dataset.
  3. Train the model by clicking the "Train Model" button.

Evaluating Your Trained Model

Once the model is trained, you can test its performance using images or your webcam. Select the "File" input type and upload different images to evaluate the model's accuracy.

Converting Your Model into TensorFlow Lite Format

To use your trained model in mobile apps, you'll need to convert it into TensorFlow Lite format. Click on the "Export Model" button, select TensorFlow Lite, and download your model in one of three formats: Floating Point, Quantized, or Edge TPU.

Conclusion

In this article, we've walked you through the entire process of training an image classification model for mobile apps using Teachable Machine. With these steps, you can create a custom AI-powered image recognition model that can be used in your mobile app. Don't forget to check out our comprehensive course on "Train Image Classification Models & Build Smart Flutter Apps 2024" and get 92% off with the coupon code "FLUTTERML92".