Optimize a ResNet101* Int8 Inference Container with TensorFlow*

Published: 12/09/2020  

Last Updated: 06/16/2022

Pull Command

docker pull intel/image-recognition:tf-latest-resnet101-int8-inference


This document has instructions for running ResNet101* int8 inference using Intel® Optimization for TensorFlow*.

Download and preprocess the ImageNet dataset using the instructions here. After running the conversion script you should have a directory with the ImageNet dataset in the TF records format.

Set the DATASET_DIR to point to this directory when running ResNet101*.

Quick Start Scripts

Script name Description
int8_online_inference Runs online inference (batch_size=1).
int8_batch_inference Runs batch inference (batch_size=128).
int8_accuracy Measures the model accuracy (batch_size=100).



The model container includes the scripts and libraries needed to run ResNet101* int8 inference. To run one of the quick start scripts using this container, you'll need to provide volume mounts for the dataset and an output directory.

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

docker run \
  --env http_proxy=${http_proxy} \
  --env https_proxy=${https_proxy} \
  --volume ${DATASET_DIR}:${DATASET_DIR} \
  --volume ${OUTPUT_DIR}:${OUTPUT_DIR} \
  --privileged --init -t \
  intel/image-recognition:tf-latest-resnet101-int8-inference \
  /bin/bash quickstart/<script name>.sh


Documentation and Sources

Get Started
Docker* Repository
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

ResNet101* Int8 Inference TensorFlow* Model Package

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Product and Performance Information


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