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143 lines
6.1 KiB
143 lines
6.1 KiB
Copyright 2017 The Android Open Source Project
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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------------------------------------------------------------------
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This directory contains models data for the Android Neural Networks API benchmarks.
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Included models:
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------------------------------------------------------------------
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- mobilenet_v1_(0.25_128|0.5_160|0.75_192|1.0_224).tflite
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MobileNet tensorflow lite model based on:
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"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"
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https://arxiv.org/abs/1704.04861
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Apache License, Version 2.0
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Downloaded from
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http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_${variant}.tgz
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on Oct 5 2018 and converted using ToT toco.
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Golden output generated with ToT tensorflow (Linux, CPU).
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------------------------------------------------------------------
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- mobilenet_v1_(0.25_128|0.5_160|0.75_192|1.0_224)_quant.tflite
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8bit quantized MobileNet tensorflow lite model based on:
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"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"
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https://arxiv.org/abs/1704.04861
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Apache License, Version 2.0
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Downloaded from
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http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_${variant}_quant.tgz
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on Oct 5 2018.
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Golden output generated with ToT tflite (Linux, CPU).
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------------------------------------------------------------------
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- mobilenet_v2_(0.35_128|0.5_160|0.75_192|1.0_224).tflite
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MobileNet v2 tensorflow lite model based on:
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"MobileNetV2: Inverted Residuals and Linear Bottlenecks"
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https://arxiv.org/abs/1801.04381
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Apache License, Version 2.0
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Downloaded from
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https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_${variant}.tgz
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on Oct 16 2018 and converted using ToT toco.
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Golden output generated with ToT tensorflow (Linux, CPU).
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------------------------------------------------------------------
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- mobilenet_v2_1.0_224_quant.tflite
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8bit quantized MobileNet v2 tensorflow lite model based on:
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"MobileNetV2: Inverted Residuals and Linear Bottlenecks"
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https://arxiv.org/abs/1801.04381
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Apache License, Version 2.0
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Downloaded from
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http://download.tensorflow.org/models/tflite_11_05_08/mobilenet_v2_1.0_224_quant.tgz
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on Oct 30 2018.
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Golden output generated with ToT tflite (Linux, CPU).
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------------------------------------------------------------------
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- ssd_mobilenet_v1_coco_float.tflite
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Float version of MobileNet SSD tensorflow model based on:
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"Speed/accuracy trade-offs for modern convolutional object detectors."
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https://arxiv.org/abs/1611.10012
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Apache License, Version 2.0
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Generated from
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http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_2018_01_28.tar.gz
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on Sep 24 2018.
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See also: https://github.com/tensorflow/models/tree/master/research/object_detection
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Golden output generated with ToT tflite (Linux, x86_64 CPU).
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------------------------------------------------------------------
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- ssd_mobilenet_v1_coco_quantized.tflite
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8bit quantized MobileNet SSD tensorflow lite model based on:
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"Speed/accuracy trade-offs for modern convolutional object detectors."
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https://arxiv.org/abs/1611.10012
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Apache License, Version 2.0
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Generated from
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http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_quantized_300x300_coco14_sync_2018_07_18.tar.gz
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on Sep 19 2018.
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See also: https://github.com/tensorflow/models/tree/master/research/object_detection
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Golden output generated with ToT tflite (Linux, CPU).
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------------------------------------------------------------------
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- tts_float.tflite
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TTS tensorflow lite model based on:
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"Fast, Compact, and High Quality LSTM-RNN Based Statistical Parametric Speech Synthesizers for
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Mobile Devices"
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https://ai.google/research/pubs/pub45379
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Apache License, Version 2.0
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Note that the tensorflow lite model is the acoustic model in the paper. It is used because it is
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much heavier than the duration model.
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------------------------------------------------------------------
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- asr_float.tflite
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ASR tensorflow lite model based on the ASR acoustic model in:
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"Personalized Speech recognition on mobile devices"
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https://arxiv.org/abs/1603.03185
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Apache License, Version 2.0
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------------------------------------------------------------------
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- mobilenet_v3-(small_224_0.75_float|small_224_1.0_float|small_224_1.0_uint8|small-minimalistic_224_1.0_float|large_224_0.75_float|large_224_1.0_float|large_224_1.0_uint8|large-minimalistic_224_1.0_float|large-minimalistic_224_1.0_uint8).tflite
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MobileNet TensorFlow Lite models based on
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"Searching for MobileNetV3"
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https://arxiv.org/abs/1905.02244
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Apache License, Version 2.0
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Downloaded from
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https://storage.googleapis.com/mobilenet_v3/checkpoints/v3-${variant}.tgz
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on Jun 30 2020.
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See also: https://github.com/tensorflow/models/tree/master/research/slim/nets/mobilenet
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Golden output generated with ToT TensorFlow (Linux, CPU).
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------------------------------------------------------------------
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Input files:
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------------------------------------------------------------------
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- ssd_mobilenet_v1_coco_*/tarmac.input
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Photo of airport tarmac by krtaylor@google.com, Apache License, Version 2.0
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- cup_(128|160|192|224).input
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Photo of cup by pszczepaniak@google.com, Apache License, Version 2.0
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- banana_(128|160|192|224).input
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Photo of banana by pszczepaniak@google.com, Apache License, Version 2.0
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- tts_float/arctic_*.input
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Linguistic features and durations generated from text sentences from the CMU Arctic set
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(http://www.festvox.org/cmu_arctic/cmuarctic.data), Apache License, Version 2.0
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- asr_float/*.input
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Acoustic features generated from audio files from the LibriSpeech dataset
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(http://www.openslr.org/12/), Creative Commons Attribution 4.0 International License
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------------------------------------------------------------------
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TODO(pszczepaniak): Provide at least 5 inputs outputs for each model
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