All models

This page lists all our trained models that are compiled for the Coral Edge TPU™.

For more information about each model type, including code examples and training scripts, refer to the model-specific pages that are linked on the Models page.

To train a custom model, using transfer learning or by building and training your own model, see our documentation about TensorFlow models on the Edge TPU.

Notice: These are not production-quality models; they are for demonstration purposes only.

Image classification (pre-trained) link

Model name Detections/Dataset Input size Depth mul. TF ver. Latency 1 Accuracy Model size Downloads

EfficientNet-EdgeTpu (L)*

1,000 objects
ILSVRC2012

300x300x3 N/A 1 21.3 ms Top-1: 81.2%
Top-5: 95.1%
12.8 MB

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

EfficientNet-EdgeTpu (M)*

1,000 objects
ILSVRC2012

240x240x3 N/A 1 7.3 ms Top-1: 80.1%
Top-5: 94.5%
8.7 MB

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

EfficientNet-EdgeTpu (S)*

1,000 objects
ILSVRC2012

224x224x3 N/A 1 5.0 ms Top-1: 78.9%
Top-5: 94.7%
6.8 MB

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

Inception V1

1,000 objects
ILSVRC2012

224x224x3 N/A 1 3.4 ms Top-1: 71.9%
Top-5: 92.0%
7.0 MB

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

Inception V3

1,000 objects
ILSVRC2012

224x224x3 N/A 1 13.4 ms Top-1: 75.4%
Top-5: 93.2%
12.0 MB

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

Inception V3

1,000 objects
ILSVRC2012

299x299x3 N/A 1 42.8 ms Top-1: 79.9%
Top-5: 95.7%
23.9 MB

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

Inception V4

1,000 objects
ILSVRC2012

299x299x3 N/A 1 84.7 ms Top-1: 80.7%
Top-5: 95.5%
42.9 MB

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

MobileNet V1

1,000 objects
ILSVRC2012

128x128x3 0.25 1 0.9 ms Top-1: 40.8%
Top-5: 67.2%
0.7 MB

Edge TPU model, CPU model,
Labels file

MobileNet V1

1,000 objects
ILSVRC2012

160x160x3 0.5 1 1.4 ms Top-1: 63.7%
Top-5: 83.4%
1.6 MB

Edge TPU model, CPU model,
Labels file

MobileNet V1

1,000 objects
ILSVRC2012

192x192x3 0.75 1 1.8 ms Top-1: 67.2%
Top-5: 88.1%
2.8 MB

Edge TPU model, CPU model,
Labels file

MobileNet V1

1,000 objects
ILSVRC2012

224x224x3 1.0 1 2.8 ms Top-1: 69.5%
Top-5: 90.6%
4.4 MB

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

MobileNet V2

900+ birds
iNaturalist 2017

224x224x3 1.0 1 2.6 ms N/A 4.1 MB

Edge TPU model, CPU model,
Labels file

MobileNet V2

1000+ insects
iNaturalist 2017

224x224x3 1.0 1 2.7 ms N/A 4.1 MB

Edge TPU model, CPU model,
Labels file

MobileNet V2

2000+ plants
iNaturalist 2017

224x224x3 1.0 1 2.6 ms N/A 5.5 MB

Edge TPU model, CPU model,
Labels file

MobileNet V2

1,000 objects
ILSVRC2012

224x224x3 1.0 1 2.9 ms Top-1: 73.2%
Top-5: 90.0%
4.0 MB

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

MobileNet V1

1,000 objects
ILSVRC2012

224x224x3 1.0 2 2.8 ms Top-1: 69.5%
Top-5: 89.8%
4.5 MB

Edge TPU model, CPU model,
Labels file

MobileNet V2

1,000 objects
ILSVRC2012

224x224x3 1.0 2 3.0 ms Top-1: 73.2%
Top-5: 91.8%
4.1 MB

Edge TPU model, CPU model,
Labels file

MobileNet V3

1,000 objects
ILSVRC2012

224x224x3 1.0 2 3.0 ms Top-1: 77.5%
Top-5: 93.6%
4.9 MB

Edge TPU model, CPU model,
Labels file

ResNet-50

1,000 objects
ILSVRC2012

224x224x3 N/A 2 42.2 ms Top-1: 73.6%
Top-5: 93.8%
25.0 MB

Edge TPU model, CPU model,
Labels file

Popular Products V1
New

100,000 popular
US products

224x224x3 N/A 1 7.0 ms N/A 9.8 MB

Edge TPU model, CPU model,
TF Hub model, Labels file

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.

* Beware that the EfficientNet family of models have unique input quantization values (scale and zero-point) that you must use when preprocessing your input. For example preprocessing code, see the classify_image.py or classify_image.cc examples.

Example code and more about image classification

Image classification (on-device training) link

Model name Training style Base dataset Input size TF ver. Model size Downloads

EfficientNet-EdgeTpu (L)

Backpropagation

1,000 objects
ILSVRC2012

300x300x3 1 11.7 MB

Edge TPU model, CPU model,
Labels file

EfficientNet-EdgeTpu (M)

Backpropagation

1,000 objects
ILSVRC2012

240x240x3 1 7.6 MB

Edge TPU model, CPU model,
Labels file

EfficientNet-EdgeTpu (S)

Backpropagation

1,000 objects
ILSVRC2012

224x224x3 1 5.7 MB

Edge TPU model, CPU model,
Labels file

MobileNet V1

Backpropagation

1,000 objects
ILSVRC2012

224x224x3 1 3.5 MB

Edge TPU model, CPU model,
Labels file

MobileNet V1

Weight imprinting

1,000 objects
ILSVRC2012

224x224x3 1 5.3 MB

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

Example code and more about image classification

Object detection link

Model name Detections/Dataset Input size TF ver. Latency 1 mAP 2 Model size Downloads

SSD MobileNet V1

90 objects
COCO

300x300x3 1 6.5 ms 21.5% 7.0 MB

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

SSD/FPN MobileNet V1
New

90 objects
COCO

640x640x3 2 229.4 ms 31.1% 37.7 MB

Edge TPU model, CPU model,
Labels file

SSD MobileNet V2

90 objects
COCO

300x300x3 1 7.3 ms 25.6% 6.6 MB

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

SSD MobileNet V2
New

90 objects
COCO

300x300x3 2 7.6 ms 22.4% 6.7 MB

Edge TPU model, CPU model,
Labels file

SSD MobileNet V2

Faces

320x320x3 1 5.2 ms N/A 6.7 MB

Edge TPU model, CPU model,
All model files

SSDLite MobileDet

90 objects
COCO

320x320x3 1 9.1 ms 32.9% 5.1 MB

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

EfficientDet-Lite0
New

90 objects
COCO

320x320x3 2 37.4 ms 30.4% 5.7 MB

Edge TPU model, CPU model,
Labels file

EfficientDet-Lite1
New

90 objects
COCO

384x384x3 2 56.3 ms 34.3% 7.6 MB

Edge TPU model, CPU model,
Labels file

EfficientDet-Lite2
New

90 objects
COCO

448x448x3 2 104.6 ms 36.0% 10.2 MB

Edge TPU model, CPU model,
Labels file

EfficientDet-Lite3
New

90 objects
COCO

512x512x3 2 107.6 ms 39.4% 14.4 MB

Edge TPU model, CPU model,
Labels file

EfficientDet-Lite3x*
New

90 objects
COCO

640x640x3 2 197.0 ms 43.9% 20.6 MB

Edge TPU model, CPU model,
Labels file

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.

* EfficientDet-Lite3x is not compatible with Edge TPUs over USB; it can be used only with PCIe-based devices. Our benchmarks for EfficientDet-Lite3x come from a desktop system paired with the Asus AI Accelerator—your results will vary.

Example code and more about object detection

Semantic segmentation link

Model name Detections/Dataset Input size Depth mul. Output stride TF ver. Latency1 Model size Downloads

U-Net MobileNet v2

37 pets
Oxford-IIIT pets

128x128x3 N/A N/A 1 2.7 ms 7.2 MB

Edge TPU model, CPU model,
Labels file

U-Net MobileNet v2

37 pets
Oxford-IIIT pets

256x256x3 N/A N/A 1 29.0 ms 7.3 MB

Edge TPU model, CPU model,
Labels file

MobileNet v2 DeepLab v3

20 objects
PASCAL VOC2012

513x513x3 0.5 N/A 1 36.8 ms 1.1 MB

Edge TPU model, CPU model,
Labels file

MobileNet v2 DeepLab v3

20 objects
PASCAL VOC2012

513x513x3 1.0 N/A 1 43.0 ms 2.9 MB

Edge TPU model, CPU model,
Labels file

EdgeTPU-DeepLab-slim
New

28 objects
Cityscapes

513x513x3 0.75 N/A 1 65.9 ms 3.1 MB

Edge TPU model, CPU model,
Model info

MobileNet v1 BodyPix

24 body parts

352x480x3 0.75 16 1 6.9 ms 1.6 MB

Edge TPU model, CPU model

MobileNet v1 BodyPix

24 body parts

512x512x3 0.75 16 1 10.7 ms 1.7 MB

Edge TPU model, CPU model

MobileNet v1 BodyPix

24 body parts

480x640x3 0.75 16 1 12.3 ms 1.8 MB

Edge TPU model, CPU model

MobileNet v1 BodyPix

24 body parts

576x768x3 0.75 16 1 17.7 ms 1.8 MB

Edge TPU model, CPU model

MobileNet v1 BodyPix

24 body parts

768x1024x3 0.75 16 1 30.8 ms 2.0 MB

Edge TPU model, CPU model

MobileNet v1 BodyPix

24 body parts

720x1280x3 0.75 16 1 38.8 ms 2.3 MB

Edge TPU model, CPU model

ResNet-50 BodyPix

24 body parts

288x416x3 N/A 16 1 46.9 ms 24.5 MB

Edge TPU model, CPU model

ResNet-50 BodyPix

24 body parts

480x640x3 N/A 16 1 384.0 ms 26.6 MB

Edge TPU model, CPU model

ResNet-50 BodyPix

24 body parts

496x768x3 N/A 32 1 87.0 ms 26.9 MB

Edge TPU model, CPU model

ResNet-50 BodyPix

24 body parts

624x864x3 N/A 32 1 153.5 ms 28.5 MB

Edge TPU model, CPU model

ResNet-50 BodyPix

24 body parts

672x928x3 N/A 16 1 737.2 ms 35.3 MB

Edge TPU model, CPU model

ResNet-50 BodyPix

24 body parts

736x960x3 N/A 32 1 N/A 38.6 MB

Edge TPU model

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.

If you want to process portrait-orientation images, download BodyPix models for portrait input.

Example code and more about semantic segmentation

Pose estimation link

Model name Detections/Dataset Input size Output stride TF ver. Latency1 Model size Downloads

PoseNet MobileNet V1

17 body points

353x481x3 16 1 5.8 ms 1.5 MB

Edge TPU model, CPU model

PoseNet MobileNet V1

17 body points

481x641x3 16 1 10.3 ms 1.7 MB

Edge TPU model, CPU model

PoseNet MobileNet V1

17 body points

721x1281x3 16 1 32.4 ms 2.5 MB

Edge TPU model, CPU model

MoveNet.SinglePose.Lightning
New

17 body points

192x192x3 4 2 7.1 ms 3.1 MB

Edge TPU model, CPU model

MoveNet.SinglePose.Thunder
New

17 body points

256x256x3 4 2 13.8 ms 7.5 MB

Edge TPU model, CPU model

PoseNet ResNet-50

17 body points

288x416x3 16 1 N/A 24.4 MB

Edge TPU model, CPU model

PoseNet ResNet-50

17 body points

480x640x3 16 1 N/A 26.4 MB

Edge TPU model, CPU model

PoseNet ResNet-50

17 body points

496x768x3 32 1 N/A 26.8 MB

Edge TPU model, CPU model

PoseNet ResNet-50

17 body points

624x864x3 32 1 N/A 28.4 MB

Edge TPU model, CPU model

PoseNet ResNet-50

17 body points

672x928x3 16 1 N/A 35.0 MB

Edge TPU model, CPU model

PoseNet ResNet-50

17 body points

736x960x3 32 1 N/A 38.5 MB

Edge TPU model, CPU model

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.

Note: BodyPix is another model that performs pose estimation, but it also provides semantic segmentation output for 24 body parts, so you can find it with the semantic segmentation models.

Example code and more about pose estimation

Speech recognition link

Model name Detections/Dataset Input size Model size Downloads

Keyword Spotter v0.8

140+ phrases

198x32x1 578 KB

Edge TPU model,
Labels file

Example code and more about speech recognition