indhub closed pull request #12397: update C++ example so it is easier to run URL: https://github.com/apache/incubator-mxnet/pull/12397
This is a PR merged from a forked repository. As GitHub hides the original diff on merge, it is displayed below for the sake of provenance: As this is a foreign pull request (from a fork), the diff is supplied below (as it won't show otherwise due to GitHub magic): diff --git a/example/image-classification/predict-cpp/README.md b/example/image-classification/predict-cpp/README.md index b3433ff2f82..69f63d70600 100644 --- a/example/image-classification/predict-cpp/README.md +++ b/example/image-classification/predict-cpp/README.md @@ -1,13 +1,23 @@ -# Image Classification Example of C++ -This is a simple predictor which shows how to use c api for image classfication. +# Image Classification Example Using the C Predict API +This is a simple predictor which shows how to use the MXNet C Predict API for image classification with a pre-trained ImageNet model. -It uses opencv for image reading +## Prerequisites -# How to Use +* OpenCV for image processing: `USE_OPENCV` is set to true by default when [building from source](https://mxnet.incubator.apache.org/install/build_from_source.html) -## Build -1. Edit image-classification-predict.cc file, change the following lines to your model paths: - ```bash +## How to Use this Example + +### Download the Model Artifacts +1. You will need the model artifacts for the Inception ImageNet model. You can download these from http://data.mxnet.io/mxnet/models/imagenet/inception-bn/ +2. Place them into a `model/Inception/` subfolder, or if not, you will need to edit the source file and update the paths in the Build step. + +* [model/Inception/Inception-BN-symbol.json](http://data.mxnet.io/mxnet/models/imagenet/inception-bn/Inception-BN-symbol.json) +* [model/Inception/Inception-BN-0126.params](http://data.mxnet.io/mxnet/models/imagenet/inception-bn/Inception-BN-0126.params) +* [model/Inception/synset.txt](http://data.mxnet.io/mxnet/models/imagenet/synset.txt) + +### Build +1. If using a different location for the model artifacts, edit `image-classification-predict.cc` file, and change the following lines to your artifacts' paths: + ```c // Models path for your model, you have to modify it std::string json_file = "model/Inception/Inception-BN-symbol.json"; std::string param_file = "model/Inception/Inception-BN-0126.params"; @@ -16,41 +26,43 @@ It uses opencv for image reading ``` 2. You may also want to change the image size and channels: - ```bash + ```c // Image size and channels int width = 224; int height = 224; int channels = 3; ``` - + 3. Simply just use our Makefile to build: ```bash make ``` -## Usage -Run: +### Run +Run the example by passing it an image that you want to classify. If you don't have one handy, run the following to get one: + ```bash - ./image-classification-predict apple.jpg + wget https://upload.wikimedia.org/wikipedia/commons/thumb/f/f4/Honeycrisp.jpg/1920px-Honeycrisp.jpg + ``` + +Then run the `image-classification-predict` program, passing the image as the argument. + + ```bash + ./image-classification-predict 1920px-Honeycrisp.jpg ``` -The only parameter is the path of the test image. ## Tips -* The model used in the sample can be downloaded here: -http://pan.baidu.com/s/1sjXKrqX -or here: -http://data.mxnet.io/mxnet/models/imagenet/ -* If you don't run it in the mxnet root path, maybe you will need to copy lib folder here. +* If you don't run it in the MXNet root path, you may need to copy the `lib` folder here. -# Author +## Author * **Xiao Liu** * E-mail: [email protected] * Homepage: [www.liuxiao.org](http://www.liuxiao.org/) -# Thanks +## Thanks * pertusa (for Makefile and image reading check) * caprice-j (for reading function) @@ -58,5 +70,3 @@ http://data.mxnet.io/mxnet/models/imagenet/ * sofiawu (for sample model) * piiswrong and tqchen (for useful coding suggestions) - - ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: [email protected] With regards, Apache Git Services
