U-Net FP32 Inference TensorFlow* Model Package

Published: 10/23/2020  

Last Updated: 06/15/2022

Download Command

wget https://storage.googleapis.com/intel-optimized-tensorflow/models/v2_3_0/unet-fp32-inference.tar.gz


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

Bare Metal

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

git clone https://github.com/jakeret/tf_unet.git
cd tf_unet/
git fetch origin pull/276/head:cpu_optimized
git checkout cpu_optimized

After installing the prerequisites, download and untar the model package. Set environment variables for the path to your TF_UNET_DIR and an OUTPUT_DIR where log files will be written, then run a quickstart script.

TF_UNET_DIR=<tensorflow-wavenet directory>
OUTPUT_DIR=<directory where log files will be written>

wget https://storage.googleapis.com/intel-optimized-tensorflow/models/v2_3_0/unet-fp32-inference.tar.gz
tar -xzf unet-fp32-inference.tar.gz
cd unet_trained


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

U-Net FP32 Inference TensorFlow* Container

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Product and Performance Information


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