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: colab.research.google.com/git...
-- Other Links --
📸 How to capture and label training data for object detection models: • How to Capture and Lab...
🏅 TFLite model comparison article: ejtech.io/learn/tflite-object...
🍓 Instructions to set up TFLite on the Raspberry Pi: • How To Run TensorFlow ...
💻 Instructions to run TFLite models on Windows: github.com/EdjeElectronics/Te...
🐜 How to quantize your TFLite model: Still to come!
📄 TFLite GitHub repository: github.com/EdjeElectronics/Te...
-- 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: • Blue Wednesday - Japan...
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