U-Net FP32 Inference TensorFlow* Container

Published: 10/23/2020  

Last Updated: 06/15/2022

Pull Command

docker pull intel/image-segmentation:tf-latest-unet-fp32-inference

Description

This document has instructionsto run a U-Net FP32 inference using Intel® Optimization for TensorFlow*.

Quick Start Scripts

Script name Description
fp32_inference Runs inference with a batch size of 1 using a pretrained model

Docker*

The model container includes the scripts and libraries needed to run U-Net FP32 inference. To run one of the quickstart scripts using this container, you'll need to provide a volume mount for the output directory.

OUTPUT_DIR=<directory where log files will be written>

docker run \
  --env OUTPUT_DIR=${OUTPUT_DIR} \
  --env http_proxy=${http_proxy} \
  --env https_proxy=${https_proxy} \
  --volume ${OUTPUT_DIR}:${OUTPUT_DIR} \
  --privileged --init -t \
  intel/image-segmentation:tf-latest-unet-fp32-inference \
  /bin/bash quickstart/fp32_inference.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.


Related Containers and Solutions

U-Net FP32 Inference TensorFlow* Model Package

View All Containers and Solutions 🡢

Product and Performance Information

1

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