STT-tensorflow/tensorflow/lite/g3doc/guide/hosted_models.md

Hosted models

The following is an incomplete list of pre-trained models optimized to work with TensorFlow Lite.

To get started choosing a model, visit Models.

Note: The best model for a given application depends on your requirements. For example, some applications might benefit from higher accuracy, while others require a small model size. You should test your application with a variety of models to find the optimal balance between size, performance, and accuracy.

Image classification

For more information about image classification, see Image classification.

Quantized models

Quantized image classification models offer the smallest model size and fastest performance, at the expense of accuracy.

Model name	Paper and model	Model size	Top-1 accuracy	Top-5 accuracy	TF Lite performance
Mobilenet_V1_0.25_128_quant	paper, tflite&pb	0.5 Mb	39.5%	64.4%	3.7 ms
Mobilenet_V1_0.25_160_quant	paper, tflite&pb	0.5 Mb	42.8%	68.1%	5.5 ms
Mobilenet_V1_0.25_192_quant	paper, tflite&pb	0.5 Mb	45.7%	70.8%	7.9 ms
Mobilenet_V1_0.25_224_quant	paper, tflite&pb	0.5 Mb	48.2%	72.8%	10.4 ms
Mobilenet_V1_0.50_128_quant	paper, tflite&pb	1.4 Mb	54.9%	78.1%	8.8 ms
Mobilenet_V1_0.50_160_quant	paper, tflite&pb	1.4 Mb	57.2%	80.5%	13.0 ms
Mobilenet_V1_0.50_192_quant	paper, tflite&pb	1.4 Mb	59.9%	82.1%	18.3 ms
Mobilenet_V1_0.50_224_quant	paper, tflite&pb	1.4 Mb	61.2%	83.2%	24.7 ms
Mobilenet_V1_0.75_128_quant	paper, tflite&pb	2.6 Mb	55.9%	79.1%	16.2 ms
Mobilenet_V1_0.75_160_quant	paper, tflite&pb	2.6 Mb	62.4%	83.7%	24.3 ms
Mobilenet_V1_0.75_192_quant	paper, tflite&pb	2.6 Mb	66.1%	86.2%	33.8 ms
Mobilenet_V1_0.75_224_quant	paper, tflite&pb	2.6 Mb	66.9%	86.9%	45.4 ms
Mobilenet_V1_1.0_128_quant	paper, tflite&pb	4.3 Mb	63.3%	84.1%	24.9 ms
Mobilenet_V1_1.0_160_quant	paper, tflite&pb	4.3 Mb	66.9%	86.7%	37.4 ms
Mobilenet_V1_1.0_192_quant	paper, tflite&pb	4.3 Mb	69.1%	88.1%	51.9 ms
Mobilenet_V1_1.0_224_quant	paper, tflite&pb	4.3 Mb	70.0%	89.0%	70.2 ms
Mobilenet_V2_1.0_224_quant	paper, tflite&pb	3.4 Mb	70.8%	89.9%	53.4 ms
Inception_V1_quant	paper, tflite&pb	6.4 Mb	70.1%	89.8%	154.5 ms
Inception_V2_quant	paper, tflite&pb	11 Mb	73.5%	91.4%	235.0 ms
Inception_V3_quant	paper,tflite&pb	23 Mb	77.5%	93.7%	637 ms
Inception_V4_quant	paper, tflite&pb	41 Mb	79.5%	93.9%	1250.8 ms

Note: The model files include both TF Lite FlatBuffer and Tensorflow frozen Graph.

Note: Performance numbers were benchmarked on Pixel-2 using single thread large core. Accuracy numbers were computed using the TFLite accuracy tool.

Floating point models

Floating point models offer the best accuracy, at the expense of model size and performance. GPU acceleration requires the use of floating point models.

Model name	Paper and model	Model size	Top-1 accuracy	Top-5 accuracy	TF Lite performance	Tensorflow performance
DenseNet	paper, tflite&pb	43.6 Mb	64.2%	85.6%	894 ms	1262 ms
SqueezeNet	paper, tflite&pb	5.0 Mb	49.0%	72.9%	224 ms	255 ms
NASNet mobile	paper, tflite&pb	21.4 Mb	73.9%	91.5%	261 ms	389 ms
NASNet large	paper, tflite&pb	355.3 Mb	82.6%	96.1%	6697 ms	7940 ms
ResNet_V2_101	paper, tflite&pb	178.3 Mb	76.8%	93.6%	1880 ms	1970 ms
Inception_V3	paper, tflite&pb	95.3 Mb	77.9%	93.8%	1433 ms	1522 ms
Inception_V4	paper, tflite&pb	170.7 Mb	80.1%	95.1%	2986 ms	3139 ms
Inception_ResNet_V2	paper, tflite&pb	121.0 Mb	77.5%	94.0%	2731 ms	2926 ms
Mobilenet_V1_0.25_128	paper, tflite&pb	1.9 Mb	41.4%	66.2%	6.2 ms	13.0 ms
Mobilenet_V1_0.25_160	paper, tflite&pb	1.9 Mb	45.4%	70.2%	8.6 ms	19.5 ms
Mobilenet_V1_0.25_192	paper, tflite&pb	1.9 Mb	47.1%	72.0%	12.1 ms	27.8 ms
Mobilenet_V1_0.25_224	paper, tflite&pb	1.9 Mb	49.7%	74.1%	16.2 ms	37.3 ms
Mobilenet_V1_0.50_128	paper, tflite&pb	5.3 Mb	56.2%	79.3%	18.1 ms	29.9 ms
Mobilenet_V1_0.50_160	paper, tflite&pb	5.3 Mb	59.0%	81.8%	26.8 ms	45.9 ms
Mobilenet_V1_0.50_192	paper, tflite&pb	5.3 Mb	61.7%	83.5%	35.6 ms	65.3 ms
Mobilenet_V1_0.50_224	paper, tflite&pb	5.3 Mb	63.2%	84.9%	47.6 ms	164.2 ms
Mobilenet_V1_0.75_128	paper, tflite&pb	10.3 Mb	62.0%	83.8%	34.6 ms	48.7 ms
Mobilenet_V1_0.75_160	paper, tflite&pb	10.3 Mb	65.2%	85.9%	51.3 ms	75.2 ms
Mobilenet_V1_0.75_192	paper, tflite&pb	10.3 Mb	67.1%	87.2%	71.7 ms	107.0 ms
Mobilenet_V1_0.75_224	paper, tflite&pb	10.3 Mb	68.3%	88.1%	95.7 ms	143.4 ms
Mobilenet_V1_1.0_128	paper, tflite&pb	16.9 Mb	65.2%	85.7%	57.4 ms	76.8 ms
Mobilenet_V1_1.0_160	paper, tflite&pb	16.9 Mb	68.0%	87.7%	86.0 ms	117.7 ms
Mobilenet_V1_1.0_192	paper, tflite&pb	16.9 Mb	69.9%	89.1%	118.6 ms	167.3 ms
Mobilenet_V1_1.0_224	paper, tflite&pb	16.9 Mb	71.0%	89.9%	160.1 ms	224.3 ms
Mobilenet_V2_1.0_224	paper, tflite&pb	14.0 Mb	71.8%	90.6%	117 ms

AutoML mobile models

The following image classification models were created using Cloud AutoML.

Model Name	Paper and model	Model size	Top-1 accuracy	Top-5 accuracy	TF Lite performance
MnasNet_0.50_224	paper, tflite&pb	8.5 Mb	68.03%	87.79%	37 ms
MnasNet_0.75_224	paper, tflite&pb	12 Mb	71.72%	90.17%	61 ms
MnasNet_1.0_96	paper, tflite&pb	17 Mb	62.33%	83.98%	23 ms
MnasNet_1.0_128	paper, tflite&pb	17 Mb	67.32%	87.70%	34 ms
MnasNet_1.0_160	paper, tflite&pb	17 Mb	70.63%	89.58%	51 ms
MnasNet_1.0_192	paper, tflite&pb	17 Mb	72.56%	90.76%	70 ms
MnasNet_1.0_224	paper, tflite&pb	17 Mb	74.08%	91.75%	93 ms
MnasNet_1.3_224	paper, tflite&pb	24 Mb	75.24%	92.55%	152 ms

Note: Performance numbers were benchmarked on Pixel-1 using single thread large BIG core.

Object detection

For more information about object detection, see Object detection.

The object detection model we currently host is coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.

Download model and labels

Pose estimation

For more information about pose estimation, see Pose estimation.

The pose estimation model we currently host is multi_person_mobilenet_v1_075_float.

Download model

Image segmentation

For more information about image segmentation, see Segmentation.

The image segmentation model we currently host is deeplabv3_257_mv_gpu.

Download model

Smart reply

For more information about smart reply, see Smart reply.

The smart reply model we currently host is smartreply_1.0_2017_11_01.

Download model