Optimize an R-FCN FP32 Inference Container with TensorFlow*

Published: 10/23/2020  

Last Updated: 06/15/2022

Pull Command

docker pull intel/object-detection:tf-latest-rfcn-fp32-inference

Description

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

The COCO validation dataset is used in these R-FCN quick start scripts. The inference quick start scripts use raw images, and the accuracy quick start scripts require the dataset to be converted into 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 inference on a directory of raw images for 500 steps and outputs performance metrics.
fp32_accuracy Processes the TF records to run inference and check accuracy on the results.

Docker*

When running in docker, the R-FCN FP32 inference container includes the libraries and the model package, which are needed to run R-FCN FP32 inference. To run the quick start scripts, you'll need to provide volume mounts for the COCO validation dataset and an output directory where log files will be written.

To run inference with performance metrics:

DATASET_DIR=<path to the coco val2017 directory>
OUTPUT_DIR=<directory where log files will be written>

docker run \
  --env DATASET_DIR=${DATASET_DIR} \
  --env OUTPUT_DIR=${OUTPUT_DIR} \
  --env http_proxy=${http_proxy} \
  --env https_proxy=${https_proxy} \
  --volume ${DATASET_DIR}:${DATASET_DIR} \
  --volume ${OUTPUT_DIR}:${OUTPUT_DIR} \
  --privileged --init -t \
  intel/object-detection:tf-latest-rfcn-fp32-inference \
  /bin/bash quickstart/<script name>.sh

To get accuracy metrics:

DATASET_DIR=<path to the COCO validation TF record directory>
OUTPUT_DIR=<directory where log files will be written>

docker run \
  --env DATASET_DIR=${DATASET_DIR} \
  --env OUTPUT_DIR=${OUTPUT_DIR} \
  --env http_proxy=${http_proxy} \
  --env https_proxy=${https_proxy} \
  --volume ${DATASET_DIR}:${DATASET_DIR} \
  --volume ${OUTPUT_DIR}:${OUTPUT_DIR} \
  --privileged --init -t \
  intel/object-detection:tf-latest-rfcn-fp32-inference \
  /bin/bash quickstart/<script name>.sh

 

Documentation and Sources

Get Started
Docker* Repository
Main GitHub*
Readme
Release Notes
Get Started Guide

Code Sources
Dockerfile
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.


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Product and Performance Information

1

Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.