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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2019/06/13 05:56:31 UTC
[GitHub] [incubator-mxnet] wuxun-zhang commented on a change in pull request
#15164: [C++] Improve inference script to support benchmark on Imagenet
wuxun-zhang commented on a change in pull request #15164: [C++] Improve inference script to support benchmark on Imagenet
URL: https://github.com/apache/incubator-mxnet/pull/15164#discussion_r293215553
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File path: cpp-package/example/inference/README.md
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@@ -30,34 +30,114 @@ To build examples use following commands:
This directory contains following examples. In order to run the examples, ensure that the path to the MXNet shared library is added to the OS specific environment variable viz. **LD\_LIBRARY\_PATH** for Linux, Mac and Ubuntu OS and **PATH** for Windows OS.
-### [inception_inference.cpp](<https://github.com/apache/incubator-mxnet/blob/master/cpp-package/example/inference/inception_inference.cpp>)
-
-This example demonstrates image classification workflow with pre-trained models using MXNet C++ API. The command line parameters the example can accept are as shown below:
-
-```
-./inception_inference --help
+## [imagenet_inference.cpp](<https://github.com/apache/incubator-mxnet/blob/master/cpp-package/example/inference/imagenet_inference.cpp>)
+
+This example demonstrates image classification workflow with pre-trained models using MXNet C++ API. Now this script also supports inference with quantized CNN models generated by Intel® MKL-DNN (see this [quantization flow](https://github.com/apache/incubator-mxnet/blob/master/example/quantization/README.md)). By using C++ API, the latency of most models can get **3%~10%** speedup compared with current Python implementation. The below tables show accuracy and latency of several CNN models.
+
+The following models have been tested on Linux systems. And 50000 images are used to collect the following accuracy numbers.
+
+| Model | Source | Dataset | FP32 Accuracy (top-1)| INT8 Accuracy (top-1)|
+|:---|:---|---|:---:|:---:|
+| [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%|69.92%|
+| [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%| 75.91% |
+| [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% | 77.05% |
+|[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%| 52.98%|
+|[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%|72.03%|
+|[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%|69.70%|
+|[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% |77.87% |
+|[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 Skylake 6148 with 20 physical cores.
Review comment:
Thanks for your suggestion. Already Done.
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