Let’s train, export, and deploy a TensorFlow Lite object detection model on the Raspberry Pi – all through a web browser using Google Colab! We’ll walk through a Colab notebook that provides start-to-finish code and instructions for training a custom TFLite model, and then show how to run it on a Raspberry Pi. The notebook uses the TensorFlow Object Detection API to train SSD-MobileNet or EfficientDet models and converts them to TFLite format.

Click this link to the Colab notebook to get started: https://colab.research.google.com/github/EdjeElectronics/TensorFlow-Lite-Object-Detection-on-Android-and-Raspberry-Pi/blob/master/Train_TFLite2_Object_Detction_Model.ipynb

— Other Links —

📸 How to capture and label training data for object detection models: https://youtu.be/v0ssiOY6cfg

🏅 TFLite model comparison article: https://ejtech.io/learn/tflite-object-detection-model-comparison

🍓 Instructions to set up TFLite on the Raspberry Pi: https://www.youtube.com/watch?v=aimSGOAUI8Y

💻 Instructions to run TFLite models on Windows: https://github.com/EdjeElectronics/TensorFlow-Lite-Object-Detection-on-Android-and-Raspberry-Pi/blob/master/deploy_guides/Windows_TFLite_Guide.md

🐜 How to quantize your TFLite model: Still to come!

📄 TFLite GitHub repository: https://github.com/EdjeElectronics/TensorFlow-Lite-Object-Detection-on-Android-and-Raspberry-Pi

— Chapters —

0:00 Introduction
1:06 Google Colab
1:41 1. Gather Training Images
3:22 2. Install TensorFlow
4:43 3. Upload Images and Prepare Data
8:41 4. Set up Training Configuration
11:20 5. Train Model
13:48 6. Convert Model to TFLite
14:20 7. Test Model
17:50 8. Deploy Model
22:07 9. Quantization
22:30 Conclusion

— Music —

– Blue Wednesday – Japanese Garden
– Provided by Lofi Records
– Watch: https://youtu.be/vJ0Sty6K2cU
– Download/Stream: https://fanlink.to/Discovery

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