From ee751a37d5accfc866d0d47ec523d0a237413209 Mon Sep 17 00:00:00 2001 From: Jitendra Patil Date: Fri, 10 Jul 2020 13:38:56 -0700 Subject: [PATCH] added review feedback --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 728ced654..cc04f952a 100644 --- a/README.md +++ b/README.md @@ -16,8 +16,8 @@ For any performance and/or benchmarking information on specific Intel platforms, ### Getting Started - If you know what model you are interested in, or if you want to see a full list of models in the Model Zoo, start **[here](/benchmarks)**. - For framework best practice guides, and step-by-step tutorials for some models in the Model Zoo, start **[here](/docs)**. -- With [Intel® AI Analytics Toolkit](https://software.intel.com/content/www/us/en/develop/tools/oneapi/ai-analytics-toolkit.html) - - Intel Model Zoo is also released as a part of Intel® AI Analytics Toolkit. Along with Model Zoo, the toolkit contains DL frameworks (Tensorflow, PyTorch), Python distribution and other software optimized on Intel architecture. +- With [Intel® AI Analytics Toolkit](https://software.intel.com/content/www/us/en/develop/tools/oneapi/ai-analytics-toolkit.html), Powered by oneAPI + - Intel Model Zoo is also released as a part of [Intel® AI Analytics Toolkit](https://software.intel.com/content/www/us/en/develop/tools/oneapi/ai-analytics-toolkit.html) which provides a consolidated package of Intel’s latest deep and machine learning optimizations all in one place for ease of development. Along with Model Zoo, the toolkit also includes Intel optimized versions of deep learning frameworks (Tensorflow, PyTorch) and high performing Python libraries to streamline end-to-end data science and AI workflows on Intel architectures. - To get started you can refer to [ResNet50 FP32 Inference code sample.](https://github.com/intel/AiKit-code-samples/tree/master/Intel_Model_Zoo_with_Tensorflow) ### Directory Structure The Model Zoo is divided into four main directories: