The following is an incomplete list of pre-trained models optimized to work with TensorFlow Lite.
To get started choosing a model, visit Models page with end-to-end examples, or pick a TensorFlow Lite model from TensorFlow Hub.
Image classification
For more information about image classification, see Image classification. Explore the TensorFlow Lite Task Library for instructions about how to integrate image classification models in just a few lines of code.
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.
You can find many quantized models from TensorFlow Hub and get more model information there.
| 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.
You can find many image classification models from TensorFlow Hub and get more model information there.
| 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.
You can find these models in TensorFlow Hub and get more model information there.
| 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. Explore the TensorFlow Lite Task Library for instructions about how to integrate object detection models in just a few lines of code.
Please find object detection models from TensorFlow Hub.
Pose estimation
For more information about pose estimation, see Pose estimation.
Please find pose estimation models from TensorFlow Hub.
Image segmentation
For more information about image segmentation, see Segmentation. Explore the TensorFlow Lite Task Library for instructions about how to integrate image segmentation models in just a few lines of code.
Please find image segmentation models from TensorFlow Hub.
Question and Answer
For more information about question and answer with MobileBERT, see Question And Answer. Explore the TensorFlow Lite Task Library for instructions about how to integrate question and answer models in just a few lines of code.
Please find Mobile BERT model from TensorFlow Hub.
Smart reply
For more information about smart reply, see Smart reply.
Please find Smart Reply model from TensorFlow Hub.