Optimize a ResNet50* V1.5 Bfloat16 Inference Container with TensorFlow*

ID 679214
Updated 6/15/2022
Version Latest



Pull Command

docker pull intel/image-recognition:tf-latest-resnet50v1-5-bfloat16-inference


This document has instructions for running ResNet50* v1.5 bfloat16 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 ResNet50 v1.5.

Quick Start Scripts

Script name Description
bfloat16_online_inference Runs online inference (batch_size=1).
bfloat16_batch_inference Runs batch inference (batch_size=128).
bfloat16_accuracy Measures the model accuracy (batch_size=100).


The model container includes the scripts and libraries needed to run ResNet50 v1.5 bfloat16 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-resnet50v1-5-bfloat16-inference \
  /bin/bash quickstart/<script name>.sh

Documentation and Sources

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

Code Sources
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License Agreement

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