Validated workloads by framework¶
We validated performance [1] for the following TensorFlow* and MXNet* workloads:
TensorFlow¶
TensorFlow Workload | Genre of Deep Learning |
---|---|
Resnet50 v1 | Image recognition |
Resnet50 v2 | Image recognition |
Inception V3 | Image recognition |
Inception V4 | Image recognition |
Inception-ResNetv2 | Image recognition |
MobileNet v1 | Image recognition |
MobileNet v2 | Image recognition |
VGG16 | Image recognition |
SSD-VGG16 | Object detection |
SSD-MobileNetv1 | Object detection |
R-FCN | Object detection |
Faster RCNN | Object detection |
Yolo v2 | Object detection |
Transformer-LT | Language translation |
Wide & Deep | Recommender system |
NCF | Recommender system |
U-Net | Image segmentation |
DCGAN | Generative adversarial network |
DRAW | Image generation |
A3C | Reinforcement learning |
MXNet¶
MXNet Workload | Genre of Deep Learning |
---|---|
Resnet50 v1 | Image recognition |
Resnet50 v2 | Image recognition |
DenseNet-121 | Image recognition |
InceptionV3 | Image recognition |
InceptionV4 | Image recognition |
Inception-ResNetv2 | Image recognition |
MobileNet v1 | Image recognition |
SqueezeNet v1 and v1.1 | Image recognition |
VGG16 | Image recognition |
Faster RCNN | Object detection |
SSD-VGG16 | Object detection |
GNMT | Language translation |
Transformer-LT | Language translation |
Wide & Deep | Recommender system |
WaveNet | Speech generation |
DeepSpeech2 | Speech recognition |
DCGAN | Generative adversarial network |
A3C | Reinforcement learning |
ONNX¶
Additionally, we validated the following workloads are functional through nGraph ONNX importer. ONNX models can be downloaded from the ONNX Model Zoo.
ONNX Workload | Genre of Deep Learning |
---|---|
ResNet-50 | Image recognition |
ResNet-50-v2 | Image recognition |
DenseNet-121 | Image recognition |
Inception-v1 | Image recognition |
Inception-v2 | Image recognition |
Mobilenet | Image recognition |
Shufflenet | Image recognition |
SqueezeNet | Image recognition |
VGG-19 | Image recognition |
ZFNet-512 | Image recognition |
MNIST | Image recognition |
Emotion-FERPlus | Image recognition |
BVLC AlexNet | Image recognition |
BVLC GoogleNet | Image recognition |
BVLC CaffeNet | Image recognition |
BVLC R-CNN ILSVRC13 | Object detection |
ArcFace | Face Detection and Recognition |
Important
Please see Intel’s Optimization Notice for details on disclaimers.
Footnotes
[1] | Benchmarking performance of DL systems is a young discipline; it is a good idea to be vigilant for results based on atypical distortions in the configuration parameters. Every topology is different, and performance changes can be attributed to multiple causes. Also watch out for the word “theoretical” in comparisons; actual performance should not be compared to theoretical performance. |