Section outline

  • AI Tools Setup and Data Collection

    🧰 Before training, you need the right tools. In this section, we focus on setting up the essential AI tools and collecting quality data — the foundation for any successful AI-powered robot. Whether you are training a vision model or a voice recognizer, good tools and clean data are key.

    • 🖥️ Setting Up Teachable Machine: Teachable Machine is a browser-based tool developed by Google that allows anyone to train machine learning models without writing code.

      1. Visit the site and select the project type (Image, Audio, or Pose).
      2. Create multiple classes (e.g., Apple, Banana, Bottle).
      3. Use your webcam or upload images to add samples to each class.
      4. Click on “Train Model” and wait for it to complete.
      5. Test the model live using your camera or upload test images.

      This is ideal for beginners and lets you export models compatible with TensorFlow Lite, which can run on Raspberry Pi, Android, or microcontrollers.

      📸 Data Collection for Vision Models:

      • Use consistent backgrounds: This helps the model focus on object features rather than surroundings.
      • Capture in varied lighting: Helps the model perform well in real environments.
      • Multiple angles and distances: Record each object from top, side, diagonal, and at various zoom levels.
      • Balanced dataset: Ensure each class (category) has a similar number of samples. Uneven data leads to bias.

      🎤 Data Collection for Audio Models:

      • Choose quiet environments with minimal background noise.
      • Record each command or word multiple times.
      • Use different voices or tones to improve generalization.
      • Ensure consistent mic distance during recordings.

      📁 Organizing Your Data:

      • Create folders for each class label (e.g., /apple/, /banana/, /bottle/).
      • Use clear filenames and consistent formats like JPG or PNG.
      • Avoid duplicates or blurry images.

      Organized datasets are easier to train, debug, and share.

    • 🔄 Data Augmentation Techniques: When you don’t have enough data, you can artificially increase it using augmentation:

      • Image Flipping or Rotation
      • Adding Blur or Noise
      • Changing Brightness or Contrast

      These methods can be done using tools like OpenCV or built-in features in Teachable Machine and TensorFlow.

      📦 Exporting Your Model:

      • Once your model is trained, export it in TensorFlow Lite (.tflite) format for use in embedded systems like Raspberry Pi or Android devices.
      • Teachable Machine also provides a “Model URL” for quick web-based deployment.

      📍Tips for Better Training:

      • Avoid training with just one background or lighting setup.
      • Include edge cases or “confusing” examples in your training set.
      • More data = Better accuracy (but with diminishing returns).
      • Retrain your model regularly as you add new data or features.

      🧪 Testing Your Model: After training, always test the model with new, unseen data. This ensures it can generalize to real-world situations. A model that performs well only on its training data is considered overfitted and unreliable.

      This section has helped you understand the importance of preparing clean, diverse datasets and using beginner-friendly tools like Teachable Machine. With your AI tools set up and data ready, we can now move toward training and deploying models on robots.