Object detection

An object detection model can identify multiple objects and their location in an image.

With the Coral Edge TPU™, you can run an object detection model directly on your device, using real-time video, at over 100 frames per second. You can even run multiple detection models concurrently on one Edge TPU, while maintaining a high frame rate.

This page provides several trained models that are compiled for the Edge TPU, example code to run them, plus information about how to train your own model with TensorFlow.

Trained models link

These models are trained and compiled for the Edge TPU.

Notice: These are not production-quality models; they are for demonstration purposes only.
Model name Detections/Dataset Input size TF ver. Latency 1 mAP 2 Model size Downloads

SSD MobileNet V1

90 objects
COCO

300x300 1 9.9 ms 21.7% 7.0 MB

Edge TPU model, CPU model,
Labels file, All model files

SSD MobileNet V2

90 objects
COCO

300x300 1 7.4 ms 25.7% 6.6 MB

Edge TPU model, CPU model,
Labels file, All model files

SSD MobileNet V2

Faces

320x320 1 5.0 ms N/A 6.7 MB

Edge TPU model, CPU model, All model files

SSDLite MobileDet

90 objects
COCO

320x320 1 8.0 ms 32.8% 5.1 MB

Edge TPU model, CPU model,
Labels file, All model files

1 Latency is the time to perform one inference, as measured with a Coral USB Accelerator on a desktop CPU. Latency varies between systems and is primarily intended for comparison between models. For more comparisons, see the Performance Benchmarks.

2 mAP is the "mean average precision," as specified by the COCO evaluation metrics. Our evaluation uses a subset of the COCO17 dataset.

Example code link

Basic object detection

An example that performs object detection with a photo and draws a square around each object. Also works with face detection models.

Languages: Python

videocam

Image recognition with video

Multiple examples showing how to stream images from a camera and run classification or detection models with the TensorFlow Lite API. Each example uses a different camera library, such as GStreamer, OpenCV, PyGame, and PiCamera.

Languages: Python

videocam

Object tracking with video

This example takes a camera feed and tracks each uniquely identified object, assigning each object with a persistent ID. The example detection script allows you to specify the tracker program you want to use (the Sort tracker is included).

Languages: Python