diff --git a/cpp-package/example/inference/README.md b/cpp-package/example/inference/README.md
index 251cd971e7f9..7a5195291a7f 100644
--- a/cpp-package/example/inference/README.md
+++ b/cpp-package/example/inference/README.md
@@ -48,7 +48,7 @@ The following models have been tested on Linux systems. And 50000 images are use
 |[ResNet152-V2](#8)|[MXNet ModelZoo](http://data.mxnet.io/models/imagenet/resnet/152-layers/)|[Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)|76.65%|76.36%|
 |[Inception-BN](#9)|[MXNet ModelZoo](http://data.mxnet.io/models/imagenet/inception-bn/)|[Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)|72.28%|72.20%|
 
-The following performance numbers are collected by using C5.18xlarge.
+The following performance numbers are collected by using AWS EC2 C5.18xlarge.
 
 | Model | Dataset |C++ latency (imgs/sec) |Python latency (imgs/sec) |
 |:---|:---|:---:|:---:|
diff --git a/cpp-package/example/inference/unit_test_imagenet_inference.sh b/cpp-package/example/inference/unit_test_imagenet_inference.sh
index b54ce3402336..c645388cd419 100755
--- a/cpp-package/example/inference/unit_test_imagenet_inference.sh
+++ b/cpp-package/example/inference/unit_test_imagenet_inference.sh
@@ -41,13 +41,13 @@ cd ..
 
 # Running inference on imagenet.
 if [ "$(uname)" == "Darwin" ]; then
-	echo ">>> INFO: FP32 real data"
+    echo ">>> INFO: FP32 real data"
     DYLD_LIBRARY_PATH=${DYLD_LIBRARY_PATH}:../../../lib ./imagenet_inference --symbol_file "./model/Inception-BN-symbol.json" --params_file "./model/Inception-BN-0126.params" --dataset "./data/val_256_q90.rec" --rgb_mean "123.68 116.779 103.939" --batch_size 1 --num_skipped_batches 50 --num_inference_batches 500
 
     echo ">>> INFO: FP32 dummy data"
     DYLD_LIBRARY_PATH=${DYLD_LIBRARY_PATH}:../../../lib ./imagenet_inference --symbol_file "./model/Inception-BN-symbol.json" --batch_size 1 --num_inference_batches 500 --benchmark
 else
-	echo ">>> INFO: FP32 real data"
+    echo ">>> INFO: FP32 real data"
     LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:../../../lib ./imagenet_inference --symbol_file "./model/Inception-BN-symbol.json" --params_file "./model/Inception-BN-0126.params" --dataset "./data/val_256_q90.rec" --rgb_mean "123.68 116.779 103.939" --batch_size 1 --num_skipped_batches 50 --num_inference_batches 500
 
     echo ">>> INFO: FP32 dummy data"
diff --git a/example/quantization/README.md b/example/quantization/README.md
index 93a14cf473ad..40f1371c33cc 100644
--- a/example/quantization/README.md
+++ b/example/quantization/README.md
@@ -27,15 +27,15 @@ The following models have been tested on Linux systems.
 
 | Model | Source | Dataset | FP32 Accuracy (top-1/top-5)| INT8 Accuracy (top-1/top-5)|
 |:---|:---|---|:---:|:---:|
-| [ResNet18-V1](#3)  | [Gluon-CV](https://gluon-cv.mxnet.io/model_zoo/classification.html)  | [Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)  |70.07%/89.30%|69.85%/89.23%|
-| [ResNet50-V1](#3)  | [Gluon-CV](https://gluon-cv.mxnet.io/model_zoo/classification.html)  | [Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)  | 75.87%/92.72%  |  75.71%/92.65% |
-| [ResNet101-V1](#3)  | [Gluon-CV](https://gluon-cv.mxnet.io/model_zoo/classification.html)  | [Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)  | 77.3%/93.58%  | 77.09%/93.41%  |
-|[Squeezenet 1.0](#4)|[Gluon-CV](https://gluon-cv.mxnet.io/model_zoo/classification.html)|[Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)|57.01%/79.71%|56.62%/79.55%|
-|[MobileNet 1.0](#5)|[Gluon-CV](https://gluon-cv.mxnet.io/model_zoo/classification.html)|[Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)|69.76%/89.32%|69.61%/89.09%|
-|[MobileNetV2 1.0](#6)|[Gluon-CV](https://gluon-cv.mxnet.io/model_zoo/classification.html)|[Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)|70.14%/89.60%|69.53%/89.24%|
-|[Inception V3](#7)|[Gluon-CV](https://gluon-cv.mxnet.io/model_zoo/classification.html)|[Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)|76.49%/93.10% |76.38%/93% |
-|[ResNet152-V2](#8)|[MXNet ModelZoo](http://data.mxnet.io/models/imagenet/resnet/152-layers/)|[Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)|76.76%/93.03%|76.48%/92.96%|
-|[Inception-BN](#9)|[MXNet ModelZoo](http://data.mxnet.io/models/imagenet/inception-bn/)|[Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)|72.09%/90.60%|72.00%/90.53%|
+| [ResNet18-V1](#3)  | [Gluon-CV](https://gluon-cv.mxnet.io/model_zoo/classification.html)  | [Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)  |70.15%/89.38%|69.92%/89.26%|
+| [ResNet50-V1](#3)  | [Gluon-CV](https://gluon-cv.mxnet.io/model_zoo/classification.html)  | [Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)  | 76.34%/93.13%  |  75.91%/92.95% |
+| [ResNet101-V1](#3)  | [Gluon-CV](https://gluon-cv.mxnet.io/model_zoo/classification.html)  | [Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)  | 77.33%/93.59%  | 77.05%/93.43%  |
+|[Squeezenet 1.0](#4)|[Gluon-CV](https://gluon-cv.mxnet.io/model_zoo/classification.html)|[Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)|56.98%/79.20%|52.98%/77.21%|
+|[MobileNet 1.0](#5)|[Gluon-CV](https://gluon-cv.mxnet.io/model_zoo/classification.html)|[Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)|72.23%/90.64%|72.03%/90.42%|
+|[MobileNetV2 1.0](#6)|[Gluon-CV](https://gluon-cv.mxnet.io/model_zoo/classification.html)|[Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)|70.27%/89.62%|69.70%/89.26%|
+|[Inception V3](#7)|[Gluon-CV](https://gluon-cv.mxnet.io/model_zoo/classification.html)|[Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)|77.76%/93.83% |77.87%/93.78% |
+|[ResNet152-V2](#8)|[MXNet ModelZoo](http://data.mxnet.io/models/imagenet/resnet/152-layers/)|[Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)|76.65%/93.07%|76.36%/92.89%|
+|[Inception-BN](#9)|[MXNet ModelZoo](http://data.mxnet.io/models/imagenet/inception-bn/)|[Validation Dataset](http://data.mxnet.io/data/val_256_q90.rec)|72.28%/90.63%|72.20%/90.56%|
 | [SSD-VGG16](#10) | [example/ssd](https://github.com/apache/incubator-mxnet/tree/master/example/ssd)  | VOC2007/2012  | 0.8366 mAP  | 0.8364 mAP  |
 | [SSD-VGG16](#10) | [example/ssd](https://github.com/apache/incubator-mxnet/tree/master/example/ssd)  | COCO2014  | 0.2552 mAP  | 0.253 mAP  |