aaronmarkham commented on a change in pull request #12808: MKL-DNN Quantization Examples and README URL: https://github.com/apache/incubator-mxnet/pull/12808#discussion_r225534872
########## File path: example/quantization/README.md ########## @@ -1,4 +1,248 @@ # Model Quantization with Calibration Examples + +This folder contains examples of quantizing a FP32 model with Intel® MKL-DNN or CUDNN. + +<h2 id="0">Contents</h2> + +* [1. Model Quantization with Intel® MKL-DNN](#1) +* [2. Model Quantization with CUDNN](#2) + +<h2 id="1">Model Quantization with Intel® MKL-DNN</h2> + +Intel® MKL-DNN supports quantization well with subgraph feature on Intel® CPU Platform and can bring huge performance improvement on Intel® Xeon® Scalable Platform. A new quantization script `imagenet_gen_qsym_mkldnn.py` has been designed to launch quantization for image-classification models with Intel® MKL-DNN. This script intergrates with [Gluon-CV modelzoo](https://gluon-cv.mxnet.io/model_zoo/classification.html) so that more pre-trained models can be downloaded from Gluon-CV and can be converted for quantization. This script also supports custom models. + +Use below command to install Gluon-CV: + +``` +pip install gluoncv +``` + +The following models have been tested on Linux systems. + +| Model | Source | Dataset | FP32 Accuracy | INT8 Accuracy | Review comment: Is this accuracy top-1 / top-5? Can you be specific? ---------------------------------------------------------------- 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
