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.