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Model Zoo for Intel® Architecture

This repository contains links to pre-trained models, sample scripts, best practices, and step-by-step tutorials for many popular open-source machine learning models optimized by Intel to run on Intel® Xeon® Scalable processors.

Model packages and containers for running the Model Zoo's workloads can be found at the Intel® oneContainer Portal.

Purpose of the Model Zoo

  • Demonstrate the AI workloads and deep learning models Intel has optimized and validated to run on Intel hardware
  • Show how to efficiently execute, train, and deploy Intel-optimized models
  • Make it easy to get started running Intel-optimized models on Intel hardware in the cloud or on bare metal

DISCLAIMER: These scripts are not intended for benchmarking Intel platforms. For any performance and/or benchmarking information on specific Intel platforms, visit https://www.intel.ai/blog.

Use cases

The model documentation in the tables below have information on the prerequisites to run each model. The model scripts run on Linux. Select models are also able to run using bare metal on Windows. For more information and a list of models that are supported on Windows, see the documentation here.

Image Recognition

Model Framework Mode Model Documentation Benchmark/Test Dataset
DenseNet169 TensorFlow Inference FP32 ImageNet 2012
Inception V3 TensorFlow Inference Int8 FP32 ImageNet 2012
Inception V4 TensorFlow Inference Int8 FP32 ImageNet 2012
MobileNet V1* TensorFlow Inference Int8 FP32 BFloat16** ImageNet 2012
ResNet 101 TensorFlow Inference Int8 FP32 ImageNet 2012
ResNet 50 TensorFlow Inference Int8 FP32 ImageNet 2012
ResNet 50v1.5 TensorFlow Inference Int8 FP32 BFloat16** ImageNet 2012
ResNet 50v1.5 TensorFlow Training FP32 BFloat16** ImageNet 2012
Inception V3 TensorFlow Serving Inference FP32 Synthetic Data
ResNet 50v1.5 TensorFlow Serving Inference FP32 Synthetic Data
GoogLeNet PyTorch Inference FP32 BFloat16** ImageNet 2012
Inception v3 PyTorch Inference FP32 BFloat16** ImageNet 2012
MNASNet 0.5 PyTorch Inference FP32 BFloat16** ImageNet 2012
MNASNet 1.0 PyTorch Inference FP32 BFloat16** ImageNet 2012
ResNet 50 PyTorch Inference FP32 BFloat16** ImageNet 2012
ResNet 50 PyTorch Training FP32 BFloat16** ImageNet 2012
ResNet 101 PyTorch Inference FP32 BFloat16** ImageNet 2012
ResNet 152 PyTorch Inference FP32 BFloat16** ImageNet 2012
ResNext 32x4d PyTorch Inference FP32 BFloat16** ImageNet 2012
ResNext 32x16d PyTorch Inference FP32 BFloat16** ImageNet 2012
VGG-11 PyTorch Inference FP32 BFloat16** ImageNet 2012
VGG-11 with batch normalization PyTorch Inference FP32 BFloat16** ImageNet 2012
Wide ResNet-50-2 PyTorch Inference FP32 BFloat16** ImageNet 2012
Wide ResNet-101-2 PyTorch Inference FP32 BFloat16** ImageNet 2012

Image Segmentation

Model Framework Mode Model Documentation Benchmark/Test Dataset
3D U-Net TensorFlow Inference FP32 BRATS 2018
3D U-Net MLPerf* TensorFlow Inference FP32 BFloat16** Int8 BRATS 2019
MaskRCNN TensorFlow Inference FP32 MS COCO 2014
UNet TensorFlow Inference FP32

Language Modeling

Model Framework Mode Model Documentation Benchmark/Test Dataset
BERT TensorFlow Inference FP32 BFloat16** SQuAD
BERT TensorFlow Training FP32 BFloat16** SQuAD and MRPC
BERT base PyTorch Inference FP32 BFloat16** BERT Base SQuAD1.1
BERT large PyTorch Inference FP32 Int8 BFloat16** BERT Large SQuAD1.1
BERT large PyTorch Training FP32 BFloat16** preprocessed text dataset
DistilBERT base PyTorch Inference FP32 BFloat16** DistilBERT Base SQuAD1.1
RNN-T PyTorch Inference FP32 BFloat16** RNN-T dataset
RNN-T PyTorch Training FP32 BFloat16** RNN-T dataset
RoBERTa base PyTorch Inference FP32 BFloat16** RoBERTa Base SQuAD 2.0

Language Translation

Model Framework Mode Model Documentation Benchmark/Test Dataset
BERT TensorFlow Inference FP32 MRPC
GNMT* TensorFlow Inference FP32 MLPerf GNMT model benchmarking dataset
Transformer_LT_mlperf* TensorFlow Training FP32 BFloat16** WMT English-German dataset
Transformer_LT_mlperf* TensorFlow Inference FP32 BFloat16** Int8 WMT English-German data
Transformer_LT_Official TensorFlow Inference FP32 WMT English-German dataset
Transformer_LT_Official TensorFlow Serving Inference FP32

Object Detection

Model Framework Mode Model Documentation Benchmark/Test Dataset
Faster R-CNN TensorFlow Inference Int8 FP32 COCO 2017 validation dataset
R-FCN TensorFlow Inference Int8 FP32 COCO 2017 validation dataset
SSD-MobileNet* TensorFlow Inference Int8 FP32 BFloat16** COCO 2017 validation dataset
SSD-ResNet34* TensorFlow Inference Int8 FP32 BFloat16** COCO 2017 validation dataset
SSD-ResNet34 TensorFlow Training FP32 BFloat16** COCO 2017 training dataset
SSD-MobileNet TensorFlow Serving Inference FP32
Faster R-CNN ResNet50 FPN PyTorch Inference FP32 BFloat16** COCO 2017
Mask R-CNN PyTorch Inference FP32 BFloat16** COCO 2017
Mask R-CNN PyTorch Training FP32 BFloat16** COCO 2017
Mask R-CNN ResNet50 FPN PyTorch Inference FP32 BFloat16** COCO 2017
RetinaNet ResNet-50 FPN PyTorch Inference FP32 BFloat16** COCO 2017
SSD-ResNet34 PyTorch Inference FP32 Int8 BFloat16** COCO 2017
SSD-ResNet34 PyTorch Training FP32 BFloat16** COCO 2017

Recommendation

Model Framework Mode Model Documentation Benchmark/Test Dataset
DIEN TensorFlow Inference FP32 BFloat16** DIEN dataset
DIEN TensorFlow Training FP32 DIEN dataset
NCF TensorFlow Inference FP32 MovieLens 1M
Wide & Deep TensorFlow Inference FP32 Census Income dataset
Wide & Deep Large Dataset TensorFlow Inference Int8 FP32 Large Kaggle Display Advertising Challenge dataset
Wide & Deep Large Dataset TensorFlow Training FP32 Large Kaggle Display Advertising Challenge dataset
DLRM PyTorch Inference FP32 Int8 BFloat16** Criteo Terabyte
DLRM PyTorch Training FP32 BFloat16** Criteo Terabyte

Reinforcement

Model Framework Mode Model Documentation Benchmark/Test Dataset
MiniGo TensorFlow Training FP32

Text-to-Speech

Model Framework Mode Model Documentation Benchmark/Test Dataset
WaveNet TensorFlow Inference FP32

Shot Boundary Detection

Model Framework Mode Model Documentation Benchmark/Test Dataset
TransNetV2 PyTorch Inference FP32 BFloat16** Synthetic Data

*Means the model belongs to MLPerf models and will be supported long-term.

How to Contribute

If you would like to add a new benchmarking script, please use this guide.