Performance Information F.A.Q.

How often do performance benchmarks get updated?

New performance benchmarks are typically published on every major.minor release of the Intel® Distribution of OpenVINO™ toolkit.

Where can I find the models used in the performance benchmarks?

All models used are included in the GitHub repository of Open Model Zoo.

Will there be any new models added to the list used for benchmarking?

The models used in the performance benchmarks were chosen based on general adoption and usage in deployment scenarios. New models that support a diverse set of workloads and usage are added periodically.

How can I run the benchmark results on my own?

All of the performance benchmarks are generated using the open-source tool within the Intel® Distribution of OpenVINO™ toolkit called benchmark_app. This tool is available for C++ apps. as well as for Python apps.

For a simple instruction on testing performance, see the Getting Performance Numbers Guide.

What image sizes are used for the classification network models?

The image size used in inference depends on the benchmarked network. The table below presents the list of input sizes for each network model:

Model	Public Network	Task	Input Size
bert-base-cased	BERT	question / answer	124
bert-large-uncased-whole-word-masking-squad-int8-0001	BERT-large	question / answer	384
deeplabv3-TF	DeepLab v3 Tf	semantic segmentation	513x513
densenet-121-TF	Densenet-121 Tf	classification	224x224
efficientdet-d0	Efficientdet	classification	512x512
faster_rcnn_resnet50_coco-TF	Faster RCNN Tf	object detection	600x1024
inception-v4-TF	Inception v4 Tf (aka GoogleNet-V4)	classification	299x299
mobilenet-ssd-CF	SSD (MobileNet)_COCO-2017_Caffe	object detection	300x300
mobilenet-v2-pytorch	Mobilenet V2 PyTorch	classification	224x224
resnet-18-pytorch	ResNet-18 PyTorch	classification	224x224
resnet-50-TF	ResNet-50_v1_ILSVRC-2012	classification	224x224
ssd-resnet34-1200-onnx	ssd-resnet34 onnx model	object detection	1200x1200
unet-camvid-onnx-0001	U-Net	semantic segmentation	368x480
yolo-v3-tiny-tf	YOLO v3 Tiny	object detection	416x416
yolo_v4-TF	Yolo-V4 TF	object detection	608x608

Where can I purchase the specific hardware used in the benchmarking?

Intel partners with vendors all over the world. For a list of Hardware Manufacturers, see the Intel® AI: In Production Partners & Solutions Catalog. For more details, see the Supported Devices. documentation. Before purchasing any hardware, you can test and run models remotely, using Intel® DevCloud for the Edge.

How can I optimize my models for better performance or accuracy?

Set of guidelines and recommendations to optimize models are available in the optimization guide. Join the conversation in the Community Forum for further support.

Why are INT8 optimized models used for benchmarking on CPUs with no VNNI support?

The benefit of low-precision optimization using the OpenVINO™ toolkit model optimizer extends beyond processors supporting VNNI through Intel® DL Boost. The reduced bit width of INT8 compared to FP32 allows Intel® CPU to process the data faster. Therefore, it offers better throughput on any converted model, regardless of the intrinsically supported low-precision optimizations within Intel® hardware. For comparison on boost factors for different network models and a selection of Intel® CPU architectures, including AVX-2 with Intel® Core™ i7-8700T, and AVX-512 (VNNI) with Intel® Xeon® 5218T and Intel® Xeon® 8270, refer to the Model Accuracy for INT8 and FP32 Precision

Where can I search for OpenVINO™ performance results based on HW-platforms?

The website format has changed in order to support more common approach of searching for the performance results of a given neural network model on different HW-platforms. As opposed to reviewing performance of a given HW-platform when working with different neural network models.

How is Latency measured?

Latency is measured by running the OpenVINO™ Runtime in synchronous mode. In this mode, each frame or image is processed through the entire set of stages (pre-processing, inference, post-processing) before the next frame or image is processed. This KPI is relevant for applications where the inference on a single image is required. For example, the analysis of an ultra sound image in a medical application or the analysis of a seismic image in the oil & gas industry. Other use cases include real or near real-time applications, e.g. the response of industrial robot to changes in its environment and obstacle avoidance for autonomous vehicles, where a quick response to the result of the inference is required.