Pull Command
docker pull intel/object-detection:tf-latest-rfcn-fp32-inference
Description
This document has instructions for running R-FCN FP32 inference using Intel® Optimization for TensorFlow*.
The COCO validation dataset is used in these R-FCN quick start scripts. The inference quick start scripts use raw images, and the accuracy quick start scripts require the dataset to be converted into the TF records format. See the COCO dataset for instructions on downloading and preprocessing the COCO validation dataset.
Quick Start Scripts
| Script name | Description |
|---|---|
fp32_inference |
Runs inference on a directory of raw images for 500 steps and outputs performance metrics. |
fp32_accuracy |
Processes the TF records to run inference and check accuracy on the results. |
Docker*
When running in docker, the R-FCN FP32 inference container includes the libraries and the model package, which are needed to run R-FCN FP32 inference. To run the quick start scripts, you'll need to provide volume mounts for the COCO validation dataset and an output directory where log files will be written.
To run inference with performance metrics:
DATASET_DIR=<path to the coco val2017 directory>
OUTPUT_DIR=<directory where log files will be written>
docker run \
--env DATASET_DIR=${DATASET_DIR} \
--env OUTPUT_DIR=${OUTPUT_DIR} \
--env http_proxy=${http_proxy} \
--env https_proxy=${https_proxy} \
--volume ${DATASET_DIR}:${DATASET_DIR} \
--volume ${OUTPUT_DIR}:${OUTPUT_DIR} \
--privileged --init -t \
intel/object-detection:tf-latest-rfcn-fp32-inference \
/bin/bash quickstart/<script name>.sh
To get accuracy metrics:
DATASET_DIR=<path to the COCO validation TF record directory>
OUTPUT_DIR=<directory where log files will be written>
docker run \
--env DATASET_DIR=${DATASET_DIR} \
--env OUTPUT_DIR=${OUTPUT_DIR} \
--env http_proxy=${http_proxy} \
--env https_proxy=${https_proxy} \
--volume ${DATASET_DIR}:${DATASET_DIR} \
--volume ${OUTPUT_DIR}:${OUTPUT_DIR} \
--privileged --init -t \
intel/object-detection:tf-latest-rfcn-fp32-inference \
/bin/bash quickstart/<script name>.sh
Documentation and Sources
Get Started
Docker* Repository
Main GitHub*
Readme
Release Notes
Get Started Guide
Code Sources
Dockerfile
Report Issue
License Agreement
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