Raspberry Pi LESSON 65: Increasing Speed of Tensorflow Lite for Object Detection

If you have been working with Raspberry Pi and Tensorflow Lite for object detection, you may have noticed that the performance can sometimes be slow. In this lesson, we will explore some techniques to increase the speed of Tensorflow Lite for object detection on Raspberry Pi.

1. Quantization

Quantization is a technique used to reduce the precision of the model parameters, which can lead to a smaller model size and faster inference. Tensorflow Lite supports post-training quantization, where the model parameters are quantized to 8-bit integers after training. This can significantly speed up the inference time on Raspberry Pi.

2. Model Optimization

Another technique to increase the speed of Tensorflow Lite for object detection is model optimization. This involves techniques such as pruning, weight sharing, and quantization-aware training to reduce the size of the model and improve inference time.

3. Hardware Acceleration

Raspberry Pi supports hardware acceleration through the use of tools such as OpenVINO and Coral which can significantly speed up the inference time for Tensorflow Lite models. These tools utilize the hardware accelerators available on the Raspberry Pi to perform inference faster than the CPU alone.

4. Profiling and Optimization

Profiling the performance of the object detection model on Raspberry Pi can help identify bottlenecks and areas for optimization. Techniques such as optimizing the input image size, batch size, and the use of tflite benchmark tool can help improve the speed of Tensorflow Lite for object detection.

5. Using a Pre-Trained Model

Using a pre-trained model for object detection can also help increase the speed of inference on Raspberry Pi. Pre-trained models are already optimized and trained on large datasets, which can result in faster inference times compared to training a model from scratch.

Conclusion

By implementing the techniques mentioned above, you can significantly increase the speed of Tensorflow Lite for object detection on Raspberry Pi. Whether it’s through quantization, model optimization, hardware acceleration, profiling, or using a pre-trained model, these techniques can help you achieve real-time object detection on Raspberry Pi.