TensorFlow Lite Python object detection example with Raspberry Pi

This example uses TensorFlow Lite with Python on a Raspberry Pi to perform real-time object detection using images streamed from the Pi Camera. It draws a bounding box around each detected object in the camera preview (when the object score is above a given threshold).

At the end of this page, there are extra steps to accelerate the example using the Coral USB Accelerator to increase inference speed.

Set up your hardware

Before you begin, you need to set up your Raspberry Pi with Raspberry Pi OS (preferably updated to Buster).

You also need to connect and configure the Pi Camera if you use the Pi Camera. This code also works with USB camera connect to the Raspberry Pi.

And to see the results from the camera, you need a monitor connected to the Raspberry Pi. It's okay if you're using SSH to access the Pi shell (you don't need to use a keyboard connected to the Pi)—you only need a monitor attached to the Pi to see the camera stream.

Download the example files

First, clone this Git repo onto your Raspberry Pi like this:

git clone https://github.com/tensorflow/examples --depth 1

Then use our script to install a couple Python packages, and download the EfficientDet-Lite model:

cd examples/lite/examples/object_detection/raspberry_pi

# The script install the required dependencies and download the TFLite models.
sh setup.sh

In this project, all you need from the TensorFlow Lite API is the Interpreter class. So instead of installing the large tensorflow package, we're using the much smaller tflite_runtime package. The setup scripts automatically install the TensorFlow Lite runtime.

Run the example

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.

Speed up model inference (optional)

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:

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