Run the Faster R-CNN FP32 Inference with a TensorFlow* Model Package

Published: 10/23/2020  

Last Updated: 06/15/2022

Download Command



This document has instructions for running Faster R-CNN FP32 inference using Intel® Optimization for TensorFlow*.

The COCO validation dataset is used in the Faster R-CNN quick start scripts. The scripts require that the dataset has been converted to the TF records format. See the COCO dataset for instructions on downloading and preprocessing the COCO validation dataset.

Quick Start Scripts

Script name Description
fp32_inference Runs batch and online inference using the coco dataset
fp32_accuracy Runs inference and evaluates the model's accuracy

Bare Metal

To run on bare metal, the following prerequisites must be installed in your environment:

  • Python* 3
  • Git
  • numactl
  • GNU Wget
  • Protobuf Compilation
  • Intel Optimizaiton for TensorFlow
  • Cython
  • contextlib2
  • Jupyter*
  • lxml
  • Matplotlib
  • Pillow 7.1.0
  • pycocotools

Clone the TensorFlow* Model Garden repository using the specified tag, and save the path to the TF_MODELS_DIR environment variable.

# Clone the TF models repo
git clone
pushd models
git checkout tags/v1.12.0
export TF_MODELS_DIR=$(pwd)

Download and extract the model package, which includes the pretrained model and scripts needed to run inference. Set environment variables for the path to your DATASET_DIR (where the coco TF records file is located) and an OUTPUT_DIR where log files will be written, then run a quick start script.

tar -xzf faster-rcnn-fp32-inference.tar.gz
cd faster-rcnn-fp32-inference

export DATASET_DIR=<path to the coco dataset>
export OUTPUT_DIR=<directory where log files will be written>

quickstart/<script name>.sh

Documentation and Sources

Get Started​
Main GitHub*
Release Notes
Get Started Guide

Code Sources
Report Issue

License Agreement

LEGAL NOTICE: By accessing, downloading or using this software and any required dependent software (the “Software Package”), you agree to the terms and conditions of the software license agreements for the Software Package, which may also include notices, disclaimers, or license terms for third party software included with the Software Package. Please refer to the license file for additional details.

Related Containers and Solutions

Faster RCNN FP32 Inference TensorFlow* Container

View All Containers and Solutions 🡢

Product and Performance Information


Performance varies by use, configuration and other factors. Learn more at