sudo sh setup.sh

python3 detect.py --model efficientdet_lite0.tflite

You should see the camera feed appear on the monitor attached to your Raspberry Pi. Put some objects in front of the camera, like a coffee mug or keyboard, and you'll see boxes drawn around those that the model recognizes, including the label and score for each. It also prints the number of frames per second (FPS) at the top-left corner of the screen. As the pipeline contains some processes other than model inference, including visualizing the detection results, you can expect a higher FPS if your inference pipeline runs in headless mode without visualization.

For more information about executing inferences with TensorFlow Lite, read TensorFlow Lite inference.

If you want to significantly speed up the inference time, you can attach an Coral USB Accelerator—a USB accessory that adds the Edge TPU ML accelerator to any Linux-based system.

If you have a Coral USB Accelerator, you can run the sample with it enabled:

1. First, be sure you have completed the USB Accelerator setup instructions.

2. Run the object detection script using the EdgeTPU TFLite model and enable the EdgeTPU option. Be noted that the EdgeTPU requires a specific TFLite model that is different from the one used above.

python3 detect.py \
  --enableEdgeTPU
  --model efficientdet_lite0_edgetpu.tflite

You should see significantly faster inference speeds.

For more information about creating and running TensorFlow Lite models with Coral devices, read TensorFlow models on the Edge TPU.