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* Added benchmark page changes

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* Added Note on the driver for Intel® Iris® Xe MAX Graphics

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53 changes: 28 additions & 25 deletions docs/benchmarks/performance_benchmarks_faq.md
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Expand Up @@ -19,31 +19,34 @@ All of the performance benchmarks were generated using the open-sourced tool wit

#### 6. What image sizes are used for the classification network models?
The image size used in the inference depends on the network being benchmarked. The following table shows the list of input sizes for each network model.
| **Model** | **Public Network** | **Task** | **Input Size** (Height x Width) |
|------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------|-----------------------------|-----------------------------------|
| [bert-large-uncased-whole-word-masking-squad](https://github.com/openvinotoolkit/open_model_zoo/tree/develop/models/intel/bert-large-uncased-whole-word-masking-squad-int8-0001) | BERT-large |question / answer |384|
| [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 |
| [facenet-20180408-102900-TF](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/facenet-20180408-102900) | FaceNet TF | face recognition | 160x160 |
| [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 |
| [googlenet-v1-TF](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/googlenet-v1-tf) | GoogLeNet_ILSVRC-2012 | classification | 224x224 |
| [inception-v3-TF](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/googlenet-v3) | Inception v3 Tf | classification | 299x299 |
| [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-v1-1.0-224-TF](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/mobilenet-v1-1.0-224-tf) | MobileNet v1 Tf | classification | 224x224 |
| [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 |
| [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 |
| [se-resnext-50-CF](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/se-resnext-50) | Se-ResNext-50_ILSVRC-2012_Caffe | classification | 224x224 |
| [squeezenet1.1-CF](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/squeezenet1.1) | SqueezeNet_v1.1_ILSVRC-2012_Caffe | classification | 227x227 |
| [ssd300-CF](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/ssd300) | SSD (VGG-16)_VOC-2007_Caffe | object detection | 300x300 |
| [yolo_v3-TF](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/yolo-v3-tf) | TF Keras YOLO v3 Modelset | object detection | 300x300 |
| [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) | ssd_mobilenet_v2 | object detection | 300x300 |
| [unet-camvid-onnx-0001](https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/intel/unet-camvid-onnx-0001/description/unet-camvid-onnx-0001.md) | U-Net | semantic segmentation | 368x480 |

| **Model** | **Public Network** | **Task** | **Input Size** (Height x Width) |
|------------------------------------------------------------------------------------------------------------------------------------|------------------------------------|-----------------------------|-----------------------------------|
| [bert-large-uncased-whole-word-masking-squad](https://github.com/openvinotoolkit/open_model_zoo/tree/develop/models/intel/bert-large-uncased-whole-word-masking-squad-int8-0001) | BERT-large |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 |
| [facenet-20180408-102900-TF](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/facenet-20180408-102900) | FaceNet TF | face recognition | 160x160 |
| [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 |
| [inception-v4-TF](https://github.com/openvinotoolkit/open_model_zoo/tree/develop/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 |
| [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 |
| [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 |
| [se-resnext-50-CF](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/se-resnext-50) | Se-ResNext-50_ILSVRC-2012_Caffe | classification | 224x224 |
| [squeezenet1.1-CF](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/squeezenet1.1) | SqueezeNet_v1.1_ILSVRC-2012_Caffe | classification | 227x227 |
| [ssd300-CF](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/ssd300) | SSD (VGG-16)_VOC-2007_Caffe | object detection | 300x300 |
| [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/blob/master/models/intel/unet-camvid-onnx-0001/description/unet-camvid-onnx-0001.md) | U-Net | semantic segmentation | 368x480 |
| [yolo-v3-tiny-tf](https://github.com/openvinotoolkit/open_model_zoo/tree/develop/models/public/yolo-v3-tiny-tf) | YOLO v3 Tiny | object detection | 416x416 |
| [ssd-resnet34-1200-onnx](https://github.com/openvinotoolkit/open_model_zoo/tree/develop/models/public/ssd-resnet34-1200-onnx) | ssd-resnet34 onnx model | object detection | 1200x1200 |
| [vgg19-caffe](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/vgg19-caffe2) | VGG-19 | classification | 224x224|

#### 7. Where can I purchase the specific hardware used in the benchmarking?
Intel partners with various vendors all over the world. Visit 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 a list of Equipment Makers and the [Supported Devices](../IE_DG/supported_plugins/Supported_Devices.md) documentation. You can also remotely test and run models before purchasing any hardware by using [Intel® DevCloud for the Edge](http://devcloud.intel.com/edge/).

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