Pull Command
docker pull intel/object-detection:tf-latest-ssd-resnet34-fp32-inference
Description
This document has instructions for running ResNet34* SSD FP32 inference using Intel® Optimization for TensorFlow*.
The ResNet34 SSD accuracy scripts (fp32_accuracy and fp32_accuracy_1200) use the COCO validation dataset in the TF records format. See the COCO dataset document for instructions on downloading and preprocessing the COCO validation dataset.
The performance benchmarking scripts (fp32_inference and fp32_inference_1200) use synthetic data, so no dataset is required.
Quick Start Scripts
| Script name | Description |
|---|---|
| fp32_accuracy | Runs an accuracy test using data in the TF records format with an input size of 300x300. |
| fp32_accuracy_1200 | Runs an accuracy test using data in the TF records format with an input size of 1200x1200. |
| fp32_inference | Runs inference with a batch size of 1 using synthetic data with an input size of 300x300. Prints out the time spent per batch and total samples/second. |
| fp32_inference_1200 | Runs inference with a batch size of 1 using synthetic data with an input size of 1200x1200. Prints out the time spent per batch and total samples/second. |
| multi_instance_batch_inference_1200 | Uses numactl to run inference (batch_size=1) with one instance per socket. Uses synthetic data with an input size of 1200x1200. Waits for all instances to complete, then prints a summarized throughput value. |
| multi_instance_online_inference_1200 | Uses numactl to run inference (batch_size=1) with 4 cores per instance. Uses synthetic data with an input size of 1200x1200. Waits for all instances to complete, then prints a summarized throughput value. |
Docker*
The model container includes the scripts and libraries needed to run ResNet34 SSD FP32 inference. To run one of the quick start scripts using this container, you'll need to provide a volume mount for the output directory. Running an accuracy test will also require a volume mount for the dataset directory (with the COCO validation dataset in the TF records format). Inference performance scripts use synthetic data.
DATASET_DIR=<path to the dataset (for accuracy testing only)>
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-ssd-resnet34-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
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.