README.txt
1Copyright 2017 The Android Open Source Project
2
3Licensed under the Apache License, Version 2.0 (the "License");
4you may not use this file except in compliance with the License.
5You may obtain a copy of the License at
6
7 http://www.apache.org/licenses/LICENSE-2.0
8
9Unless required by applicable law or agreed to in writing, software
10distributed under the License is distributed on an "AS IS" BASIS,
11WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12See the License for the specific language governing permissions and
13limitations under the License.
14------------------------------------------------------------------
15
16This directory contains models data for the Android Neural Networks API benchmarks.
17
18Included models:
19
20------------------------------------------------------------------
21- mobilenet_v1_(0.25_128|0.5_160|0.75_192|1.0_224).tflite
22MobileNet tensorflow lite model based on:
23"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"
24https://arxiv.org/abs/1704.04861
25Apache License, Version 2.0
26
27Downloaded from
28http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_${variant}.tgz
29on Oct 5 2018 and converted using ToT toco.
30Golden output generated with ToT tensorflow (Linux, CPU).
31
32------------------------------------------------------------------
33- mobilenet_v1_(0.25_128|0.5_160|0.75_192|1.0_224)_quant.tflite
348bit quantized MobileNet tensorflow lite model based on:
35"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"
36https://arxiv.org/abs/1704.04861
37Apache License, Version 2.0
38
39Downloaded from
40http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_${variant}_quant.tgz
41on Oct 5 2018.
42Golden output generated with ToT tflite (Linux, CPU).
43
44------------------------------------------------------------------
45- mobilenet_v2_(0.35_128|0.5_160|0.75_192|1.0_224).tflite
46MobileNet v2 tensorflow lite model based on:
47"MobileNetV2: Inverted Residuals and Linear Bottlenecks"
48https://arxiv.org/abs/1801.04381
49Apache License, Version 2.0
50
51Downloaded from
52https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_${variant}.tgz
53on Oct 16 2018 and converted using ToT toco.
54Golden output generated with ToT tensorflow (Linux, CPU).
55
56------------------------------------------------------------------
57- mobilenet_v2_1.0_224_quant.tflite
588bit quantized MobileNet v2 tensorflow lite model based on:
59"MobileNetV2: Inverted Residuals and Linear Bottlenecks"
60https://arxiv.org/abs/1801.04381
61Apache License, Version 2.0
62
63Downloaded from
64http://download.tensorflow.org/models/tflite_11_05_08/mobilenet_v2_1.0_224_quant.tgz
65on Oct 30 2018.
66Golden output generated with ToT tflite (Linux, CPU).
67
68------------------------------------------------------------------
69- ssd_mobilenet_v1_coco_float.tflite
70Float version of MobileNet SSD tensorflow model based on:
71"Speed/accuracy trade-offs for modern convolutional object detectors."
72https://arxiv.org/abs/1611.10012
73Apache License, Version 2.0
74
75Generated from
76http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_2018_01_28.tar.gz
77on Sep 24 2018.
78See also: https://github.com/tensorflow/models/tree/master/research/object_detection
79Golden output generated with ToT tflite (Linux, x86_64 CPU).
80
81------------------------------------------------------------------
82- ssd_mobilenet_v1_coco_quantized.tflite
838bit quantized MobileNet SSD tensorflow lite model based on:
84"Speed/accuracy trade-offs for modern convolutional object detectors."
85https://arxiv.org/abs/1611.10012
86Apache License, Version 2.0
87
88Generated from
89http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_quantized_300x300_coco14_sync_2018_07_18.tar.gz
90on Sep 19 2018.
91See also: https://github.com/tensorflow/models/tree/master/research/object_detection
92Golden output generated with ToT tflite (Linux, CPU).
93
94------------------------------------------------------------------
95- tts_float.tflite
96TTS tensorflow lite model based on:
97"Fast, Compact, and High Quality LSTM-RNN Based Statistical Parametric Speech Synthesizers for
98Mobile Devices"
99https://ai.google/research/pubs/pub45379
100Apache License, Version 2.0
101
102Note that the tensorflow lite model is the acoustic model in the paper. It is used because it is
103much heavier than the duration model.
104------------------------------------------------------------------
105- asr_float.tflite
106ASR tensorflow lite model based on the ASR acoustic model in:
107"Personalized Speech recognition on mobile devices"
108https://arxiv.org/abs/1603.03185
109Apache License, Version 2.0
110
111------------------------------------------------------------------
112Input files:
113------------------------------------------------------------------
114- ssd_mobilenet_v1_coco_*/tarmac.input
115Photo of airport tarmac by krtaylor@google.com, Apache License, Version 2.0
116- cup_(128|160|192|224).input
117Photo of cup by pszczepaniak@google.com, Apache License, Version 2.0
118- banana_(128|160|192|224).input
119Photo of banana by pszczepaniak@google.com, Apache License, Version 2.0
120- tts_float/arctic_*.input
121Linguistic features and durations generated from text sentences from the CMU Arctic set
122(http://www.festvox.org/cmu_arctic/cmuarctic.data), Apache License, Version 2.0
123- asr_float/*.input
124Acoustic features generated from audio files from the LibriSpeech dataset
125(http://www.openslr.org/12/), Creative Commons Attribution 4.0 International License
126------------------------------------------------------------------
127
128TODO(pszczepaniak): Provide at least 5 inputs outputs for each model
129
130