diff --git a/tensorflow/lite/g3doc/guide/hosted_models.md b/tensorflow/lite/g3doc/guide/hosted_models.md index 5304fe7a455..5a52b37e59a 100644 --- a/tensorflow/lite/g3doc/guide/hosted_models.md +++ b/tensorflow/lite/g3doc/guide/hosted_models.md @@ -3,7 +3,9 @@ The following is an incomplete list of pre-trained models optimized to work with TensorFlow Lite. -To get started choosing a model, visit <a href="../models">Models</a>. +To get started choosing a model, visit <a href="../models">Models</a> page with +end-to-end examples, or pick a [TensorFlow Lite model from TensorFlow Hub] +(https://tfhub.dev/s?deployment-format=lite). Note: The best model for a given application depends on your requirements. For example, some applications might benefit from higher accuracy, while others @@ -22,6 +24,9 @@ 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](https://tfhub.dev/s?deployment-format=lite&module-type=image-classification&q=quantized) +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](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.25_128_quant.tgz) | 0.5 Mb | 39.5% | 64.4% | 0.8 ms | 2 ms @@ -60,6 +65,9 @@ performance. <a href="../performance/gpu">GPU acceleration</a> 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](https://tfhub.dev/s?deployment-format=lite&module-type=image-classification) +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](https://arxiv.org/abs/1608.06993), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/densenet_2018_04_27.tgz) | 43.6 Mb | 64.2% | 85.6% | 195 ms | 60 ms | 1656 ms @@ -94,6 +102,9 @@ The following image classification models were created using <a href="https://cloud.google.com/automl/">Cloud AutoML</a>. The performance values are measured on Pixel 3 on Android 10. +You can find these models in [TensorFlow Hub](https://tfhub.dev/s?deployment-format=lite&q=MnasNet) +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](https://arxiv.org/abs/1807.11626), [tflite&pb](https://storage.cloud.google.com/download.tensorflow.org/models/tflite/mnasnet_0.5_224_09_07_2018.tgz) | 8.5 Mb | 68.03% | 87.79% | 9.5 ms | 5.9 ms | 16.6 ms @@ -114,44 +125,37 @@ Accuracy numbers were computed using the For more information about object detection, see <a href="../models/object_detection/overview.md">Object detection</a>. -The object detection model we currently host is -**coco_ssd_mobilenet_v1_1.0_quant_2018_06_29**. - -<a class="button button-primary" href="https://storage.googleapis.com/download.tensorflow.org/models/tflite/coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.zip">Download -model and labels</a> +Please find [object detection models](https://tfhub.dev/s?deployment-format=lite&module-type=image-object-detection) +from TensorFlow Hub. ## Pose estimation For more information about pose estimation, see <a href="../models/pose_estimation/overview.md">Pose estimation</a>. -The pose estimation model we currently host is -**multi_person_mobilenet_v1_075_float**. - -<a class="button button-primary" href="https://storage.googleapis.com/download.tensorflow.org/models/tflite/gpu/multi_person_mobilenet_v1_075_float.tflite">Download -model</a> +Please find [pose estimation models](https://tfhub.dev/s?deployment-format=lite&module-type=image-pose-detection) +from TensorFlow Hub. ## Image segmentation For more information about image segmentation, see <a href="../models/segmentation/overview.md">Segmentation</a>. -The image segmentation model we currently host is **deeplabv3_257_mv_gpu**. - -<a class="button button-primary" href="https://storage.googleapis.com/download.tensorflow.org/models/tflite/gpu/deeplabv3_257_mv_gpu.tflite">Download -model</a> +Please find [image segmentation models](https://tfhub.dev/s?deployment-format=lite&module-type=image-segmentation) +from TensorFlow Hub. ## Question and Answer For more information about text classification with Mobile BERT, see <a href="../models/bert_qa/overview.md">Question And Answer</a>. +Please find [Mobile BERT model](https://tfhub.dev/tensorflow/mobilebert/1) from +TensorFlow Hub. + ## Smart reply For more information about smart reply, see <a href="../models/smart_reply/overview.md">Smart reply</a>. -The smart reply model we currently host is **smartreply_1.0_2017_11_01**. - -<a class="button button-primary" href="https://storage.googleapis.com/download.tensorflow.org/models/smartreply_1.0_2017_11_01.zip">Download -model</a> +Please find [Smart Reply model](https://tfhub.dev/tensorflow/smartreply/1) from +TensorFlow Hub.