Added definitions to all models

models/image_classification/mobilenet_v2_1.0_224/tflite_uint8/README.md

@@ -1,7 +1,7 @@
 # MobileNet v2 1.0 224 UINT8
 
 ## Description
-MobileNet v2 is an efficient image classification neural network, targeted for mobile and embedded usecases. This model is trained on the ImageNet dataset and is quantized to the UINT8 datatype by Google.
+MobileNet v2 is an efficient image classification neural network, targeted for mobile and embedded use cases. This model is trained on the ImageNet dataset and is quantized to the UINT8 datatype by Google.
 
 ## License
 [Apache-2.0](https://spdx.org/licenses/Apache-2.0.html)
@@ -13,7 +13,7 @@ MobileNet v2 is an efficient image classification neural network, targeted for m
 |  SHA-1 Hash         | 275c9649cb395139103b6d15f53011b1b949ad00 |
 |  Size (Bytes)       | 3577760 |
 |  Provenance         | https://tfhub.dev/tensorflow/lite-model/mobilenet_v2_1.0_224_quantized/1/default/1 |
-|  Paper              | https://arxiv.org/pdf/1704.04861.pdf |
+|  Paper              | https://arxiv.org/pdf/1801.04381.pdf |
 
 ## Accuracy
 Dataset: Ilsvrc 2012

models/image_classification/mobilenet_v2_1.0_224/tflite_uint8/definition.yaml

@@ -0,0 +1,38 @@
+benchmark:
+  ILSVRC 2012:
+    top_1_accuracy: 0.708
+description: MobileNet v2 is an efficient image classification neural network, targeted
+  for mobile and embedded use cases. This model is trained on the ImageNet dataset
+  and is quantized to the UINT8 datatype by Google.
+license: Apache-2.0
+network:
+  file_size_bytes: 3577760
+  filename: mobilenet_v2_1.0_224_quantized_1_default_1.tflite
+  framework: TensorFlow Lite
+  hash:
+    algorithm: sha1
+    value: 275c9649cb395139103b6d15f53011b1b949ad00
+  provenance: https://tfhub.dev/tensorflow/lite-model/mobilenet_v2_1.0_224_quantized/1/default/1
+network_parameters:
+  input_nodes:
+  - description: Single 224x224 RGB image with UINT8 values between 0 and 255
+    name: input
+    shape:
+    - 1
+    - 224
+    - 224
+    - 3
+  output_nodes:
+  - description: Per-class confidence for 1001 ImageNet classes
+    name: output
+    shape:
+    - 1
+    - 1001
+operators:
+  TensorFlow Lite:
+  - ADD
+  - AVERAGE_POOL_2D
+  - CONV_2D
+  - DEPTHWISE_CONV_2D
+  - RESHAPE
+paper: https://arxiv.org/pdf/1801.04381.pdf

models/keyword_spotting/cnn_large/tflite_int8/definition.yaml

@@ -0,0 +1,39 @@
+benchmark:
+  Google Speech Commands test set:
+    Accuracy: 92.92%
+description: 'This is a fully quantized version (asymmetrical int8) of the CNN Large
+  model developed by Arm, with training checkpoints, from the Hello Edge paper. Code
+  to recreate this model can be found here: https://github.com/ARM-software/ML-examples/tree/master/tflu-kws-cortex-m'
+license:
+- Apache-2.0
+network:
+  file_size_bytes: 486560
+  filename: cnn_l_quantized.tflite
+  framework: TensorFlow Lite
+  hash:
+    algorithm: sha1
+    value: 59e6986c3eca496fa3d54176ac66bb7dc9ff36e0
+  provenance: https://github.com/ARM-software/ML-examples/tree/master/tflu-kws-cortex-m
+network_parameters:
+  input_nodes:
+  - description: The input is a processed MFCCs of shape (1, 490)
+    name: input
+    shape:
+    - 1
+    - 490
+  output_nodes:
+  - description: The probability on 12 keywords.
+    name: Identity
+    shape:
+    - 1
+    - 12
+operators:
+  TensorFlow Lite:
+  - CONV_2D
+  - DEQUANTIZE
+  - FULLY_CONNECTED
+  - QUANTIZE
+  - RELU
+  - RESHAPE
+  - SOFTMAX
+paper: https://arxiv.org/abs/1711.07128

models/keyword_spotting/cnn_medium/tflite_int8/definition.yaml

@@ -0,0 +1,39 @@
+benchmark:
+  Google Speech Commands test set:
+    Accuracy: 91.33%
+description: 'This is a fully quantized version (asymmetrical int8) of the CNN Medium
+  model developed by Arm, with training checkpoints, from the Hello Edge paper. Code
+  to recreate this model can be found here: https://github.com/ARM-software/ML-examples/tree/master/tflu-kws-cortex-m'
+license:
+- Apache-2.0
+network:
+  file_size_bytes: 187840
+  filename: cnn_m_quantized.tflite
+  framework: TensorFlow Lite
+  hash:
+    algorithm: sha1
+    value: 389c6c2c7d289c0018e2dabcc66271811e52874c
+  provenance: https://github.com/ARM-software/ML-examples/tree/master/tflu-kws-cortex-m
+network_parameters:
+  input_nodes:
+  - description: The input is a processed MFCCs of shape (1, 490)
+    name: input
+    shape:
+    - 1
+    - 490
+  output_nodes:
+  - description: The probability on 12 keywords.
+    name: Identity
+    shape:
+    - 1
+    - 12
+operators:
+  TensorFlow Lite:
+  - CONV_2D
+  - DEQUANTIZE
+  - FULLY_CONNECTED
+  - QUANTIZE
+  - RELU
+  - RESHAPE
+  - SOFTMAX
+paper: https://arxiv.org/abs/1711.07128

models/keyword_spotting/cnn_small/tflite_int8/definition.yaml

@@ -0,0 +1,39 @@
+benchmark:
+  Google Speech Commands test set:
+    Accuracy: 91.41%
+description: 'This is a fully quantized version (asymmetrical int8) of the CNN Small
+  model developed by Arm, with training checkpoints, from the Hello Edge paper. Code
+  to recreate this model can be found here: https://github.com/ARM-software/ML-examples/tree/master/tflu-kws-cortex-m'
+license:
+- Apache-2.0
+network:
+  file_size_bytes: 76752
+  filename: cnn_s_quantized.tflite
+  framework: TensorFlow Lite
+  hash:
+    algorithm: sha1
+    value: d3c8f4b468545d7012383f2a312bef6245a3b599
+  provenance: https://github.com/ARM-software/ML-examples/tree/master/tflu-kws-cortex-m
+network_parameters:
+  input_nodes:
+  - description: The input is a processed MFCCs of shape (1, 490)
+    name: input
+    shape:
+    - 1
+    - 490
+  output_nodes:
+  - description: The probability on 12 keywords.
+    name: Identity
+    shape:
+    - 1
+    - 12
+operators:
+  TensorFlow Lite:
+  - CONV_2D
+  - DEQUANTIZE
+  - FULLY_CONNECTED
+  - QUANTIZE
+  - RELU
+  - RESHAPE
+  - SOFTMAX
+paper: https://arxiv.org/abs/1711.07128

models/keyword_spotting/dnn_large/tflite_int8/definition.yaml

@@ -0,0 +1,37 @@
+benchmark:
+  Google Speech Commands test set:
+    Accuracy: 86.28%
+description: 'This is a fully quantized version (asymmetrical int8) of the DNN Large
+  model developed by Arm, with training checkpoints, from the Hello Edge paper. Code
+  to recreate this model can be found here: https://github.com/ARM-software/ML-examples/tree/master/tflu-kws-cortex-m'
+license:
+- Apache-2.0
+network:
+  file_size_bytes: 502928
+  filename: dnn_l_quantized.tflite
+  framework: TensorFlow Lite
+  hash:
+    algorithm: sha1
+    value: 16e03dda20ae81dfba6a567e6e7563ca67596969
+  provenance: https://github.com/ARM-software/ML-examples/tree/master/tflu-kws-cortex-m
+network_parameters:
+  input_nodes:
+  - description: The input is a processed MFCCs of shape (1, 250)
+    name: input
+    shape:
+    - 1
+    - 250
+  output_nodes:
+  - description: The probability on 12 keywords.
+    name: Identity
+    shape:
+    - 1
+    - 12
+operators:
+  TensorFlow Lite:
+  - DEQUANTIZE
+  - FULLY_CONNECTED
+  - QUANTIZE
+  - RELU
+  - SOFTMAX
+paper: https://arxiv.org/abs/1711.07128

models/keyword_spotting/dnn_medium/tflite_int8/definition.yaml

@@ -0,0 +1,37 @@
+benchmark:
+  Google Speech Commands test set:
+    Accuracy: 84.64%
+description: 'This is a fully quantized version (asymmetrical int8) of the DNN Medium
+  model developed by Arm, with training checkpoints, from the Hello Edge paper. Code
+  to recreate this model can be found here: https://github.com/ARM-software/ML-examples/tree/master/tflu-kws-cortex-m'
+license:
+- Apache-2.0
+network:
+  file_size_bytes: 204480
+  filename: dnn_m_quantized.tflite
+  framework: TensorFlow Lite
+  hash:
+    algorithm: sha1
+    value: 57ad3cf78f736819b8897f5de51f7e9a4cbd5689
+  provenance: https://github.com/ARM-software/ML-examples/tree/master/tflu-kws-cortex-m
+network_parameters:
+  input_nodes:
+  - description: The input is a processed MFCCs of shape (1, 250)
+    name: input
+    shape:
+    - 1
+    - 250
+  output_nodes:
+  - description: The probability on 12 keywords.
+    name: Identity
+    shape:
+    - 1
+    - 12
+operators:
+  TensorFlow Lite:
+  - DEQUANTIZE
+  - FULLY_CONNECTED
+  - QUANTIZE
+  - RELU
+  - SOFTMAX
+paper: https://arxiv.org/abs/1711.07128

models/keyword_spotting/dnn_small/tflite_int8/definition.yaml

@@ -0,0 +1,37 @@
+benchmark:
+  Google Speech Commands test set:
+    Accuracy: 82.70%
+description: 'This is a fully quantized version (asymmetrical int8) of the DNN Small
+  model developed by Arm, with training checkpoints, from the Hello Edge paper. Code
+  to recreate this model can be found here: https://github.com/ARM-software/ML-examples/tree/master/tflu-kws-cortex-m'
+license:
+- Apache-2.0
+network:
+  file_size_bytes: 84192
+  filename: dnn_s_quantized.tflite
+  framework: TensorFlow Lite
+  hash:
+    algorithm: sha1
+    value: 5b00a7eb54eb2650c50026ddef2b3134a71ab6cf
+  provenance: https://github.com/ARM-software/ML-examples/tree/master/tflu-kws-cortex-m
+network_parameters:
+  input_nodes:
+  - description: The input is a processed MFCCs of shape (1, 250)
+    name: input
+    shape:
+    - 1
+    - 250
+  output_nodes:
+  - description: The probability on 12 keywords.
+    name: Identity
+    shape:
+    - 1
+    - 12
+operators:
+  TensorFlow Lite:
+  - DEQUANTIZE
+  - FULLY_CONNECTED
+  - QUANTIZE
+  - RELU
+  - SOFTMAX
+paper: https://arxiv.org/abs/1711.07128

models/keyword_spotting/ds_cnn_large/tflite_int8/definition.yaml

@@ -0,0 +1,41 @@
+benchmark:
+  Google Speech Commands test set:
+    Accuracy: 94.58%
+description: 'This is a fully quantized version (asymmetrical int8) of the DS-CNN
+  Large model developed by Arm, with training checkpoints, from the Hello Edge paper.
+  Code to recreate this model can be found here: https://github.com/ARM-software/ML-examples/tree/master/tflu-kws-cortex-m'
+license:
+- Apache-2.0
+network:
+  file_size_bytes: 530688
+  filename: ds_cnn_l_quantized.tflite
+  framework: TensorFlow Lite
+  hash:
+    algorithm: sha1
+    value: abaa9d4bf8797801276c00151ee14426aa1b2dcc
+  provenance: https://github.com/ARM-software/ML-examples/tree/master/tflu-kws-cortex-m
+network_parameters:
+  input_nodes:
+  - description: The input is a processed MFCCs of shape (1, 490)
+    name: input
+    shape:
+    - 1
+    - 490
+  output_nodes:
+  - description: The probability on 12 keywords.
+    name: Identity
+    shape:
+    - 1
+    - 12
+operators:
+  TensorFlow Lite:
+  - AVERAGE_POOL_2D
+  - CONV_2D
+  - DEPTHWISE_CONV_2D
+  - DEQUANTIZE
+  - FULLY_CONNECTED
+  - QUANTIZE
+  - RELU
+  - RESHAPE
+  - SOFTMAX
+paper: https://arxiv.org/abs/1711.07128

models/keyword_spotting/ds_cnn_medium/tflite_int8/definition.yaml

@@ -0,0 +1,41 @@
+benchmark:
+  Google Speech Commands test set:
+    Accuracy: 93.35%
+description: 'This is a fully quantized version (asymmetrical int8) of the DS-CNN
+  Medium model developed by Arm, with training checkpoints, from the Hello Edge paper.
+  Code to recreate this model can be found here: https://github.com/ARM-software/ML-examples/tree/master/tflu-kws-cortex-m'
+license:
+- Apache-2.0
+network:
+  file_size_bytes: 200928
+  filename: ds_cnn_m_quantized.tflite
+  framework: TensorFlow Lite
+  hash:
+    algorithm: sha1
+    value: c6923b02806224775b58ab9bc11e03e021ff407e
+  provenance: https://github.com/ARM-software/ML-examples/tree/master/tflu-kws-cortex-m
+network_parameters:
+  input_nodes:
+  - description: The input is a processed MFCCs of shape (1, 490)
+    name: input
+    shape:
+    - 1
+    - 490
+  output_nodes:
+  - description: The probability on 12 keywords.
+    name: Identity
+    shape:
+    - 1
+    - 12
+operators:
+  TensorFlow Lite:
+  - AVERAGE_POOL_2D
+  - CONV_2D
+  - DEPTHWISE_CONV_2D
+  - DEQUANTIZE
+  - FULLY_CONNECTED
+  - QUANTIZE
+  - RELU
+  - RESHAPE
+  - SOFTMAX
+paper: https://arxiv.org/abs/1711.07128