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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.

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

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

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