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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.

Question and Answer

For more information about text classification with Mobile BERT, see Question And Answer.

Quantized models

Quantized image classification models offer the smallest model size and fastest performance, at the expense of accuracy. The performance values are measured on Pixel 3 on Android 10.

Model name Paper and model Model size Top-1 accuracy Top-5 accuracy CPU, 4 threads NNAPI
Mobilenet_V1_0.25_128_quant paper, tflite&pb 0.5 Mb 39.5% 64.4% 0.8 ms 2 ms
Mobilenet_V1_0.25_160_quant paper, tflite&pb 0.5 Mb 42.8% 68.1% 1.3 ms 2.4 ms
Mobilenet_V1_0.25_192_quant paper, tflite&pb 0.5 Mb 45.7% 70.8% 1.8 ms 2.6 ms
Mobilenet_V1_0.25_224_quant paper, tflite&pb 0.5 Mb 48.2% 72.8% 2.3 ms 2.9 ms
Mobilenet_V1_0.50_128_quant paper, tflite&pb 1.4 Mb 54.9% 78.1% 1.7 ms 2.6 ms
Mobilenet_V1_0.50_160_quant paper, tflite&pb 1.4 Mb 57.2% 80.5% 2.6 ms 2.9 ms
Mobilenet_V1_0.50_192_quant paper, tflite&pb 1.4 Mb 59.9% 82.1% 3.6 ms 3.3 ms
Mobilenet_V1_0.50_224_quant paper, tflite&pb 1.4 Mb 61.2% 83.2% 4.7 ms 3.6 ms
Mobilenet_V1_0.75_128_quant paper, tflite&pb 2.6 Mb 55.9% 79.1% 3.1 ms 3.2 ms
Mobilenet_V1_0.75_160_quant paper, tflite&pb 2.6 Mb 62.4% 83.7% 4.7 ms 3.8 ms
Mobilenet_V1_0.75_192_quant paper, tflite&pb 2.6 Mb 66.1% 86.2% 6.4 ms 4.2 ms
Mobilenet_V1_0.75_224_quant paper, tflite&pb 2.6 Mb 66.9% 86.9% 8.5 ms 4.8 ms
Mobilenet_V1_1.0_128_quant paper, tflite&pb 4.3 Mb 63.3% 84.1% 4.8 ms 3.8 ms
Mobilenet_V1_1.0_160_quant paper, tflite&pb 4.3 Mb 66.9% 86.7% 7.3 ms 4.6 ms
Mobilenet_V1_1.0_192_quant paper, tflite&pb 4.3 Mb 69.1% 88.1% 9.9 ms 5.2 ms
Mobilenet_V1_1.0_224_quant paper, tflite&pb 4.3 Mb 70.0% 89.0% 13 ms 6.0 ms
Mobilenet_V2_1.0_224_quant paper, tflite&pb 3.4 Mb 70.8% 89.9% 12 ms 6.9 ms
Inception_V1_quant paper, tflite&pb 6.4 Mb 70.1% 89.8% 39 ms 36 ms
Inception_V2_quant paper, tflite&pb 11 Mb 73.5% 91.4% 59 ms 18 ms
Inception_V3_quant paper,tflite&pb 23 Mb 77.5% 93.7% 148 ms 74 ms
Inception_V4_quant paper, tflite&pb 41 Mb 79.5% 93.9% 268 ms 155 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. The performance values are measured on Pixel 3 on Android 10.

Model name Paper and model Model size Top-1 accuracy Top-5 accuracy CPU, 4 threads GPU NNAPI
DenseNet paper, tflite&pb 43.6 Mb 64.2% 85.6% 195 ms 60 ms 1656 ms
SqueezeNet paper, tflite&pb 5.0 Mb 49.0% 72.9% 36 ms 9.5 ms 18.5 ms
NASNet mobile paper, tflite&pb 21.4 Mb 73.9% 91.5% 56 ms --- 102 ms
NASNet large paper, tflite&pb 355.3 Mb 82.6% 96.1% 1170 ms --- 648 ms
ResNet_V2_101 paper, tflite&pb 178.3 Mb 76.8% 93.6% 526 ms 92 ms 1572 ms
Inception_V3 paper, tflite&pb 95.3 Mb 77.9% 93.8% 249 ms 56 ms 148 ms
Inception_V4 paper, tflite&pb 170.7 Mb 80.1% 95.1% 486 ms 93 ms 291 ms
Inception_ResNet_V2 paper, tflite&pb 121.0 Mb 77.5% 94.0% 422 ms 100 ms 201 ms
Mobilenet_V1_0.25_128 paper, tflite&pb 1.9 Mb 41.4% 66.2% 1.2 ms 1.6 ms 3 ms
Mobilenet_V1_0.25_160 paper, tflite&pb 1.9 Mb 45.4% 70.2% 1.7 ms 1.7 ms 3.2 ms
Mobilenet_V1_0.25_192 paper, tflite&pb 1.9 Mb 47.1% 72.0% 2.4 ms 1.8 ms 3.0 ms
Mobilenet_V1_0.25_224 paper, tflite&pb 1.9 Mb 49.7% 74.1% 3.3 ms 1.8 ms 3.6 ms
Mobilenet_V1_0.50_128 paper, tflite&pb 5.3 Mb 56.2% 79.3% 3.0 ms 1.7 ms 3.2 ms
Mobilenet_V1_0.50_160 paper, tflite&pb 5.3 Mb 59.0% 81.8% 4.4 ms 2.0 ms 4.0 ms
Mobilenet_V1_0.50_192 paper, tflite&pb 5.3 Mb 61.7% 83.5% 6.0 ms 2.5 ms 4.8 ms
Mobilenet_V1_0.50_224 paper, tflite&pb 5.3 Mb 63.2% 84.9% 7.9 ms 2.8 ms 6.1 ms
Mobilenet_V1_0.75_128 paper, tflite&pb 10.3 Mb 62.0% 83.8% 5.5 ms 2.6 ms 5.1 ms
Mobilenet_V1_0.75_160 paper, tflite&pb 10.3 Mb 65.2% 85.9% 8.2 ms 3.1 ms 6.3 ms
Mobilenet_V1_0.75_192 paper, tflite&pb 10.3 Mb 67.1% 87.2% 11.0 ms 4.5 ms 7.2 ms
Mobilenet_V1_0.75_224 paper, tflite&pb 10.3 Mb 68.3% 88.1% 14.6 ms 4.9 ms 9.9 ms
Mobilenet_V1_1.0_128 paper, tflite&pb 16.9 Mb 65.2% 85.7% 9.0 ms 4.4 ms 6.3 ms
Mobilenet_V1_1.0_160 paper, tflite&pb 16.9 Mb 68.0% 87.7% 13.4 ms 5.0 ms 8.4 ms
Mobilenet_V1_1.0_192 paper, tflite&pb 16.9 Mb 69.9% 89.1% 18.1 ms 6.3 ms 10.6 ms
Mobilenet_V1_1.0_224 paper, tflite&pb 16.9 Mb 71.0% 89.9% 24.0 ms 6.5 ms 13.8 ms
Mobilenet_V2_1.0_224 paper, tflite&pb 14.0 Mb 71.8% 90.6% 17.5 ms 6.2 ms 11.23 ms

AutoML mobile models

The following image classification models were created using Cloud AutoML. The performance values are measured on Pixel 3 on Android 10.

Model Name Paper and model Model size Top-1 accuracy Top-5 accuracy CPU, 4 threads GPU NNAPI
MnasNet_0.50_224 paper, tflite&pb 8.5 Mb 68.03% 87.79% 9.5 ms 5.9 ms 16.6 ms
MnasNet_0.75_224 paper, tflite&pb 12 Mb 71.72% 90.17% 13.7 ms 7.1 ms 16.7 ms
MnasNet_1.0_96 paper, tflite&pb 17 Mb 62.33% 83.98% 5.6 ms 5.4 ms 12.1 ms
MnasNet_1.0_128 paper, tflite&pb 17 Mb 67.32% 87.70% 7.5 ms 5.8 ms 12.9 ms
MnasNet_1.0_160 paper, tflite&pb 17 Mb 70.63% 89.58% 11.1 ms 6.7 ms 14.2 ms
MnasNet_1.0_192 paper, tflite&pb 17 Mb 72.56% 90.76% 14.5 ms 7.7 ms 16.6 ms
MnasNet_1.0_224 paper, tflite&pb 17 Mb 74.08% 91.75% 19.4 ms 8.7 ms 19 ms
MnasNet_1.3_224 paper, tflite&pb 24 Mb 75.24% 92.55% 27.9 ms 10.6 ms 22.0 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