Month 3 Box - AI Deep Dive

Lesson 7: Training Your Own AI Image Recognition Model

This lesson takes everything you’ve learned about computer vision and makes it custom. Today, your Raspberry Pi learns how to recognize objects you choose—whether it’s your logo, your water bottle, or your favorite snack.


🧠 What You’ll Learn Today:

  • How AI image recognition works behind the scenes
  • How to collect and label your own training dataset
  • How to train an image classifier using YOLOv11 or TensorFlow Lite
  • How to run your custom-trained model on the Raspberry Pi
  • How to build a personalized object detector that runs in real time


📸 How AI Sees the World:

  • Training = feeding the AI labeled examples of objects
  • Inference = using that trained model to detect objects in new images


📷 Collecting Your Dataset:

Use:

  • The Pi Camera
  • Your smartphone
  • Licensed images from the internet


Tip: Aim for 10–20 images per class with varied angles, lighting, and backgrounds.


Don’t worry about perfect coverage—augmentation helps fill in the gaps.


🏷️ Labeling Tools:

  • Roboflow (Free, web-based, easy export for YOLO and TFLite)
  • LabelImg (Open-source desktop labeling tool)


🧪 Training Your Model:

  • Use YOLOv11 (Python-based, fast and accurate)
  • Or TensorFlow/Keras (for .tflite output)
  • Too slow on the Pi? Train in the cloud or on your PC, then copy the model to your Pi when done.


🎯 Demo Ideas:

  • Detect your company or club logo
  • Classify different types of snacks
  • Find your favorite object on your desk


🔧 Hands-On Activity:

  1. Collect 20+ images for a single object
  2. Label them using Roboflow or LabelImg
  3. Train a model using YOLOv11 or TensorFlow
  4. Run your trained model on the Pi and test it live


🛠️ Troubleshooting Tips:


  • 🧠 Low accuracy?

 → Add more training data

 → Use better labeling

 → Vary lighting/backgrounds


  • 🐢 Running slow?

 → Quantize the model

 → Use fewer classes


📝 Homework:

  • Finish training your custom object detection model
  • Run it live with your Pi camera
  • Share a short video of it working in the #month3 Discord channel

🔥 Bonus: Try training the model to recognize two object types!


🚀 Up Next:

Next lesson, we’ll start connecting all these capabilities into a full AI assistant framework—combining vision, voice, and reasoning.