Merge pull request #31 from ARM-software/add_accuracy_scores

Added accuracy scores to table

Author: tom-arm

README.md

@@ -1,6 +1,6 @@
 benchmark:
   DCASE 2020 Task 2 Slide rail:
-    AUC: 0.9632
+    AUC: 0.963
 description: This is a fully quantized version (asymmetrical int8) of the MicroNet
   Medium model developed by Arm, from the MicroNets paper. It is trained on the 'slide
   rail' task from http://dcase.community/challenge2020/task-unsupervised-detection-of-anomalous-sounds.

models/anomaly_detection/micronet_medium/tflite_int8/README.md

@@ -1,6 +1,6 @@
 benchmark:
   DCASE 2020 Task 2 Slide rail:
-    AUC: 0.9548
+    AUC: 0.955
 description: This is a fully quantized version (asymmetrical int8) of the MicroNet
   Small model developed by Arm, from the MicroNets paper. It is trained on the 'slide
   rail' task from http://dcase.community/challenge2020/task-unsupervised-detection-of-anomalous-sounds.

models/anomaly_detection/micronet_medium/tflite_int8/definition.yaml

@@ -1,6 +1,6 @@
 benchmark:
   ILSVRC 2012:
-    top-1-accuracy: '69.68'
+    top-1-accuracy: 0.697
 description: "INT8 quantised version of MobileNet v2 model. Trained on ImageNet."
 license:
 - Apache-2.0

models/anomaly_detection/micronet_small/tflite_int8/README.md

@@ -1,6 +1,6 @@
 benchmark:
   Google Speech Commands test set:
-    Accuracy: 92.92%
+    Accuracy: 0.929
 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'

models/anomaly_detection/micronet_small/tflite_int8/definition.yaml

@@ -1,6 +1,6 @@
 benchmark:
   Google Speech Commands test set:
-    Accuracy: 91.33%
+    Accuracy: 0.913
 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'

models/image_classification/mobilenet_v2_1.0_224/tflite_int8/README.md

@@ -1,6 +1,6 @@
 benchmark:
   Google Speech Commands test set:
-    Accuracy: 91.41%
+    Accuracy: 0.914
 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'

models/image_classification/mobilenet_v2_1.0_224/tflite_int8/definition.yaml

@@ -1,6 +1,6 @@
 benchmark:
   Google Speech Commands test set:
-    Accuracy: 86.28%
+    Accuracy: 0.863
 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'

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

@@ -1,6 +1,6 @@
 benchmark:
   Google Speech Commands test set:
-    Accuracy: 84.64%
+    Accuracy: 0.846
 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'

models/keyword_spotting/cnn_large/tflite_int8/README.md

@@ -1,6 +1,6 @@
 benchmark:
   Google Speech Commands test set:
-    Accuracy: 82.70%
+    Accuracy: 0.827
 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'

models/keyword_spotting/cnn_large/tflite_int8/definition.yaml

@@ -1,6 +1,6 @@
 benchmark:
   SpeechCommands:
-    top_1_accuracy: 0.9495
+    top_1_accuracy: 0.950
 description: 'This is a clustered (32 clusters, kmeans++ centroid initialization)
   and retrained (fine-tuned) FP32 version of the DS-CNN Large model developed by Arm
   from the Hello Edge paper. Code for the original DS-CNN implementation can be found

models/keyword_spotting/cnn_medium/tflite_int8/README.md

@@ -1,6 +1,6 @@
 benchmark:
   SpeechCommands:
-    top_1_accuracy: 0.9401
+    top_1_accuracy: 0.940
 description: 'This is a clustered (32 clusters, kmeans++ centroid initialization),
   retrained (fine-tuned) and fully quantized version (INT8) of the DS-CNN Large model
   developed by Arm from the Hello Edge paper. Code for the original DS-CNN implementation

models/keyword_spotting/cnn_medium/tflite_int8/definition.yaml

@@ -1,6 +1,6 @@
 benchmark:
   Google Speech Commands test set:
-    Accuracy: 94.58%
+    Accuracy: 0.946
 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'

models/keyword_spotting/cnn_small/tflite_int8/README.md

@@ -1,6 +1,6 @@
 benchmark:
   Google Speech Commands test set:
-    Accuracy: 93.35%
+    Accuracy: 0.934
 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'

models/keyword_spotting/cnn_small/tflite_int8/definition.yaml

@@ -1,6 +1,6 @@
 benchmark:
   Google Speech Commands test set:
-    Accuracy: 93.35%
+    Accuracy: 0.934
 description: 'This is a fully quantized version (asymmetrical int8) of the DS-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'

models/keyword_spotting/dnn_large/tflite_int8/README.md

@@ -1,6 +1,6 @@
 benchmark:
   Google Speech Commands test set:
-    Accuracy: 96.48%
+    Accuracy: 0.965
 description: This is a fully quantized version (asymmetrical int8) of the MicroNet
   Large model developed by Arm, from the MicroNets paper. This model is trained on
   the 'Google Speech Commands' dataset.

models/keyword_spotting/dnn_large/tflite_int8/definition.yaml

@@ -1,6 +1,6 @@
 benchmark:
   Google Speech Commands test set:
-    Accuracy: 95.77%
+    Accuracy: 0.958
 description: This is a fully quantized version (asymmetrical int8) of the MicroNet
   Medium model developed by Arm, from the MicroNets paper. This model is trained on
   the 'Google Speech Commands' dataset.

models/keyword_spotting/dnn_medium/tflite_int8/README.md

@@ -1,6 +1,6 @@
 benchmark:
   Google Speech Commands test set:
-    Accuracy: 95.32%
+    Accuracy: 0.953
 description: This is a fully quantized version (asymmetrical int8) of the MicroNet
   Small model developed by Arm, from the MicroNets paper. This model is trained on
   the 'Google Speech Commands' dataset.

models/keyword_spotting/dnn_medium/tflite_int8/definition.yaml

@@ -1,6 +1,6 @@
 benchmark:
   coco_validation_2017:
-    mAP: 0.21
+    mAP: 0.210
 description: SSD MobileNet v1 is a object detection network, that localizes and identifies
   objects in an input image. This is a TF Lite floating point version that takes a
   300x300 input image and outputs detections for this image. This model is trained

models/keyword_spotting/dnn_small/tflite_int8/README.md

@@ -1,6 +1,6 @@
 benchmark:
   COCO 2017 Validation:
-    mAP: '0.234'
+    mAP: 0.234
 description: SSD MobileNet v1 is a object detection network, that localizes and identifies
   objects in an input image. This is a TF Lite quantized version that takes a 300x300
   input image and outputs detections for this image. This model is converted from

models/keyword_spotting/dnn_small/tflite_int8/definition.yaml

@@ -1,6 +1,6 @@
 benchmark:
   coco_validation_2017:
-    mAP: 0.18
+    mAP: 0.180
 description: SSD MobileNet v1 is a object detection network, that localizes and identifies
   objects in an input image. This is a TF Lite quantized version that takes a 300x300
   input image and outputs detections for this image. This model is trained and quantized

models/keyword_spotting/ds_cnn_large/tflite_clustered_fp32/README.md

@@ -1,6 +1,6 @@
 benchmark:
   LibriSpeech:
-    LER: 0.08771
+    LER: 0.0877
 description: Wav2letter is a convolutional speech recognition neural network. This
   implementation was created by Arm and quantized to the INT8 datatype.
 license: