openvino/docs/benchmarks/performance_benchmarks_faq.md

# Performance Information Frequently Asked Questions {#openvino_docs_performance_benchmarks_faq}

The following questions (Q#) and answers (A) are related to published [performance benchmarks](./performance_benchmarks.md).

#### Q1: How often do performance benchmarks get updated?
**A**: New performance benchmarks are typically published on every `major.minor` release of the Intel® Distribution of OpenVINO™ toolkit.

#### Q2: Where can I find the models used in the performance benchmarks?
**A**: All models used are included in the GitHub repository of [Open Model Zoo](https://github.com/openvinotoolkit/open_model_zoo).

#### Q3: Will there be any new models added to the list used for benchmarking?
**A**: 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.

#### Q4: What does "CF" or "TF" in the graphs stand for?
**A**: The "CF" means "Caffe", and "TF" means "TensorFlow".

#### Q5: How can I run the benchmark results on my own?
**A**: All of the performance benchmarks were generated using the open-source tool within the Intel® Distribution of OpenVINO™ toolkit called `benchmark_app`. This tool is available in both [C++](../../samples/cpp/benchmark_app/README.md) and [Python](../../tools/benchmark_tool/README.md).

#### Q6: What image sizes are used for the classification network models?
**A**: 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** (Height x Width)   |
|------------------------------------------------------------------------------------------------------------------------------------|------------------------------------|-----------------------------|-----------------------------------|
| [bert-base-cased](https://github.com/PaddlePaddle/PaddleNLP/tree/v2.1.1)                                                           | BERT                               | question / answer           | 124                               |
| [bert-large-uncased-whole-word-masking-squad-int8-0001](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/intel/bert-large-uncased-whole-word-masking-squad-int8-0001) | BERT-large  | question / answer | 384                   |
| [bert-small-uncased-whole-masking-squad-0002](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/intel/bert-small-uncased-whole-word-masking-squad-0002) | BERT-small | question / answer        | 384  |
| [brain-tumor-segmentation-0001-MXNET](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/brain-tumor-segmentation-0001) | brain-tumor-segmentation-0001 | semantic segmentation       | 128x128x128 |
| [brain-tumor-segmentation-0002-CF2](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/brain-tumor-segmentation-0002)   | brain-tumor-segmentation-0002 | semantic segmentation       | 128x128x128 |
| [deeplabv3-TF](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/deeplabv3)                                    |  DeepLab v3 Tf                        | semantic segmentation      | 513x513                          |
| [densenet-121-TF](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/densenet-121-tf)                  | Densenet-121 Tf                        | classification              | 224x224                 |
| [efficientdet-d0](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/efficientdet-d0-tf)               | Efficientdet                          | classification | 512x512 |
| [facenet-20180408-102900-TF](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/facenet-20180408-102900)        | FaceNet TF                            | face recognition            | 160x160                        |
| [Facedetection0200](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/intel/face-detection-0200)                | FaceDetection0200 | detection | 256x256 |
| [faster_rcnn_resnet50_coco-TF](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/faster_rcnn_resnet50_coco)    | Faster RCNN Tf                        | object detection            | 600x1024               |
| [forward-tacotron-duration-prediction](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/forward-tacotron) | ForwardTacotron | text to speech | 241 |
| [inception-v4-TF](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/googlenet-v4-tf)          | Inception v4 Tf (aka GoogleNet-V4)    | classification              | 299x299          |
| [inception-v3-TF](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/googlenet-v3)                | Inception v3 Tf                       | classification              | 299x299          |
| [mask_rcnn_resnet50_atrous_coco](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/mask_rcnn_resnet50_atrous_coco) | Mask R-CNN ResNet50 Atrous | instance segmentation | 800x1365 |
| [mobilenet-ssd-CF](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/mobilenet-ssd)                  | SSD (MobileNet)_COCO-2017_Caffe       | object detection            | 300x300             |
| [mobilenet-v2-1.0-224-TF](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/mobilenet-v2-1.0-224)        | MobileNet v2 Tf                       | classification              | 224x224             |
| [mobilenet-v2-pytorch](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/mobilenet-v2-pytorch )      | Mobilenet V2 PyTorch                  | classification              | 224x224               |
| [Mobilenet-V3-small](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/mobilenet-v3-small-1.0-224-tf) | Mobilenet-V3-1.0-224 | classifier | 224x224 |
| [Mobilenet-V3-large](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/mobilenet-v3-large-1.0-224-tf) | Mobilenet-V3-1.0-224 | classifier | 224x224 |
| [pp-ocr-rec](https://github.com/PaddlePaddle/PaddleOCR/tree/release/2.1/)                                                       | PP-OCR | optical character recognition | 32x640 |
| [pp-yolo](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1)                                                     | PP-YOLO                                | detection | 640x640 |
| [resnet-18-pytorch](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/resnet-18-pytorch)                  | ResNet-18 PyTorch                     | classification              | 224x224             |
| [resnet-50-pytorch](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/resnet-50-pytorch)              | ResNet-50 v1 PyTorch                  | classification              | 224x224                        |
| [resnet-50-TF](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/resnet-50-tf)                  | ResNet-50_v1_ILSVRC-2012              | classification              | 224x224             |
| [yolo_v4-TF](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/yolo-v4-tf)                            | Yolo-V4 TF                            |  object detection          | 608x608                        |
| [ssd_mobilenet_v1_coco-TF](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/ssd_mobilenet_v1_coco)   | ssd_mobilenet_v1_coco                 | object detection            | 300x300                        |
| [ssdlite_mobilenet_v2-TF](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/ssdlite_mobilenet_v2)     | ssdlite_mobilenet_v2                  | object detection            | 300x300                        |
| [unet-camvid-onnx-0001](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/intel/unet-camvid-onnx-0001) | U-Net  | semantic segmentation       | 368x480                        |
| [yolo-v3-tiny-tf](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/yolo-v3-tiny-tf)                 | YOLO v3 Tiny                          | object detection            | 416x416 |
| [yolo-v3](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/yolo-v3-tf)                               | YOLO v3                               | object detection            | 416x416 |
| [ssd-resnet34-1200-onnx](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/ssd-resnet34-1200-onnx)   | ssd-resnet34 onnx model               | object detection            | 1200x1200 |

#### Q7: Where can I purchase the specific hardware used in the benchmarking?
**A**: Intel partners with vendors all over the world.  For a list of Hardware Manufacturers, see the [Intel® AI: In Production Partners & Solutions Catalog](https://www.intel.com/content/www/us/en/internet-of-things/ai-in-production/partners-solutions-catalog.html) . For more details, see the [Supported Devices](../OV_Runtime_UG/supported_plugins/Supported_Devices.md) documentation. Before purchasing any hardware, you can test and run models remotely, using [Intel® DevCloud for the Edge](http://devcloud.intel.com/edge/).

#### Q8: How can I optimize my models for better performance or accuracy?
**A**: Set of guidelines and recommendations to optimize models are available in the [optimization guide](../optimization_guide/dldt_optimization_guide.md). Join the conversation in the [Community Forum](https://software.intel.com/en-us/forums/intel-distribution-of-openvino-toolkit) for further support.

#### Q9: Why are INT8 optimized models used for benchmarking on CPUs with no VNNI support?
**A**: 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](performance_int8_vs_fp32.md) article.

#### Q10: Where can I search for OpenVINO™ performance results based on HW-platforms?
**A**: 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.

#### Q11: How is Latency measured?
**A**: 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.