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.

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	300x300	N/A	1	24.5 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	240x240	N/A	1	8.4 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	224x224	N/A	1	4.9 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	224x224	N/A	1	3.1 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	224x224	N/A	1	13.0 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	299x299	N/A	1	42.2 ms	Top-1: 79.9% Top-5: 95.7%	24.0 MB	Edge TPU model, CPU model, Labels file, All model files
Inception V4	1,000 objects ILSVRC2012	299x299	N/A	1	84.0 ms	Top-1: 80.5% Top-5: 95.7%	43.0 MB	Edge TPU model, CPU model, Labels file, All model files
MobileNet V1	1,000 objects ILSVRC2012	128x128	0.25	1	0.6 ms	Top-1: 41.2% Top-5: 66.6%	0.7 MB	Edge TPU model, CPU model, Labels file
MobileNet V1	1,000 objects ILSVRC2012	160x160	0.5	1	1.0 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	192x192	0.75	1	1.4 ms	Top-1: 67.2% Top-5: 88.1%	2.9 MB	Edge TPU model, CPU model, Labels file
MobileNet V1	1,000 objects ILSVRC2012	224x224	1.0	1	2.3 ms	Top-1: 69.5% Top-5: 90.6%	4.5 MB	Edge TPU model, CPU model, Labels file, All model files
MobileNet V2	900+ birds iNaturalist 2017	224x224	1.0	1	2.5 ms	N/A	4.1 MB	Edge TPU model, CPU model, Labels file
MobileNet V2	1000+ insects iNaturalist 2017	224x224	1.0	1	2.5 ms	N/A	4.1 MB	Edge TPU model, CPU model, Labels file
MobileNet V2	2000+ plants iNaturalist 2017	224x224	1.0	1	2.6 ms	N/A	5.5 MB	Edge TPU model, CPU model, Labels file
MobileNet V2	1,000 objects ILSVRC2012	224x224	1.0	1	2.5 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	224x224	1.0	2	2.4 ms	Top-1: 69.5% Top-5: 89.8%	4.6 MB	Edge TPU model, CPU model, Labels file
MobileNet V2	1,000 objects ILSVRC2012	224x224	1.0	2	2.5 ms	Top-1: 73.2% Top-5: 91.8%	4.2 MB	Edge TPU model, CPU model, Labels file
MobileNet V3	1,000 objects ILSVRC2012	224x224	1.0	2	2.7 ms	Top-1: 77.5% Top-5: 93.6%	5.0 MB	Edge TPU model, CPU model, Labels file
ResNet-50	1,000 objects ILSVRC2012	224x224	N/A	2	42.9 ms	Top-1: 73.6% Top-5: 93.8%	25.0 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.

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	300x300	1	11.7 MB	Edge TPU model, CPU model, Labels file
EfficientNet-EdgeTpu (M)	Backpropagation	1,000 objects ILSVRC2012	240x240	1	7.6 MB	Edge TPU model, CPU model, Labels file
EfficientNet-EdgeTpu (S)	Backpropagation	1,000 objects ILSVRC2012	224x224	1	5.7 MB	Edge TPU model, CPU model, Labels file
MobileNet V1	Backpropagation	1,000 objects ILSVRC2012	224x224	1	3.5 MB	Edge TPU model, CPU model, Labels file
MobileNet V1	Weight imprinting	1,000 objects ILSVRC2012	224x224	1	5.4 MB	Edge TPU model, CPU model, Labels file, All model files

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	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.

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	128x128	N/A	N/A	1	3.6 ms	7.2 MB	Edge TPU model, CPU model, Labels file
U-Net MobileNet v2	37 pets Oxford-IIIT pets	256x256	N/A	N/A	1	27.7 ms	7.3 MB	Edge TPU model, CPU model, Labels file
MobileNet v2 DeepLab v3	20 objects PASCAL VOC2012	513x513	0.5	N/A	1	43.3 ms	1.1 MB	Edge TPU model, CPU model, Labels file
MobileNet v2 DeepLab v3	20 objects PASCAL VOC2012	513x513	1.0	N/A	1	49.4 ms	2.9 MB	Edge TPU model, CPU model, Labels file
MobileNet v1 BodyPix	24 body parts	512x512	0.75	16	1	10.7 ms	1.7 MB	Edge TPU model
MobileNet v1 BodyPix	24 body parts	480x352	0.75	16	1	N/A	1.6 MB	Edge TPU model
MobileNet v1 BodyPix	24 body parts	640x480	0.75	16	1	N/A	1.8 MB	Edge TPU model
MobileNet v1 BodyPix	24 body parts	768x576	0.75	16	1	N/A	1.8 MB	Edge TPU model
MobileNet v1 BodyPix	24 body parts	1024x768	0.75	16	1	N/A	2.0 MB	Edge TPU model
MobileNet v1 BodyPix	24 body parts	1280x720	0.75	16	1	N/A	2.3 MB	Edge TPU model
ResNet-50 BodyPix	24 body parts	416x288	N/A	16	1	N/A	24.5 MB	Edge TPU model
ResNet-50 BodyPix	24 body parts	640x480	N/A	16	1	N/A	26.6 MB	Edge TPU model
ResNet-50 BodyPix	24 body parts	768x496	N/A	32	1	N/A	26.9 MB	Edge TPU model
ResNet-50 BodyPix	24 body parts	864x624	N/A	32	1	N/A	28.5 MB	Edge TPU model
ResNet-50 BodyPix	24 body parts	928x672	N/A	16	1	N/A	35.3 MB	Edge TPU model
ResNet-50 BodyPix	24 body parts	960x736	N/A	32	1	N/A	38.6 MB	Edge TPU model

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 parts	353x481	16	1	5.9 ms	1.5 MB	Edge TPU model, CPU model
PoseNet MobileNet V1	17 body parts	481x641	16	1	10.3 ms	1.7 MB	Edge TPU model, CPU model
PoseNet MobileNet V1	17 body parts	721x1281	16	1	32.9 ms	2.5 MB	Edge TPU model, CPU model
PoseNet ResNet-50	17 body parts	288x416	16	1	N/A	24.4 MB	Edge TPU model, CPU model
PoseNet ResNet-50	17 body parts	480x640	16	1	N/A	26.4 MB	Edge TPU model, CPU model
PoseNet ResNet-50	17 body parts	496x768	32	1	N/A	26.8 MB	Edge TPU model, CPU model
PoseNet ResNet-50	17 body parts	624x864	32	1	N/A	28.4 MB	Edge TPU model, CPU model
PoseNet ResNet-50	17 body parts	672x928	16	1	N/A	35.0 MB	Edge TPU model, CPU model
PoseNet ResNet-50	17 body parts	736x960	32	1	N/A	38.5 MB	Edge TPU model, CPU model

More about pose estimation

Speech recognition link

Model name	Detections/Dataset	Input size	Model size	Downloads
Keyword Spotter v0.8	140+ phrases	32x198	578 KB	Edge TPU model, Labels file

More about speech recognition

More resources link

• Build your own model for the Edge TPU
• Try our retraining tutorials (Colab notebooks)