.. frameworks/validated/list.rst: ################################# Validated workloads by framework ################################# We validated performance [#f1]_ for the following TensorFlow\* and MXNet\* workloads: * :ref:`tensorflow_valid` * :ref:`mxnet_valid` * :ref:`onnx_valid` * :doc:`testing-latency` .. _tensorflow_valid: TensorFlow ========== .. csv-table:: :header: "TensorFlow Workload", "Genre of Deep Learning" :widths: 27, 53 :escape: ~ 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_valid: MXNet ===== .. csv-table:: :header: "MXNet Workload", "Genre of Deep Learning" :widths: 27, 53 :escape: ~ 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_valid: ONNX ==== Additionally, we validated the following workloads are functional through `nGraph ONNX importer`_. ONNX models can be downloaded from the `ONNX Model Zoo`_. .. csv-table:: :header: "ONNX Workload", "Genre of Deep Learning" :widths: 27, 53 :escape: ~ 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. .. rubric:: Footnotes .. [#f1] 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. .. _Optimization Notice: https://software.intel.com/en-us/articles/optimization-notice .. _nGraph ONNX importer: https://github.com/NervanaSystems/ngraph-onnx/blob/master/README.md .. _ONNX Model Zoo: https://github.com/onnx/models .. Notice revision #20110804: Intel's compilers may or may not optimize to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel microprocessors. These optimizations include SSE2, SSE3, and SSSE3 instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel. Microprocessor-dependent optimizations in this product are intended for use with Intel microprocessors. Certain optimizations not specific to Intel microarchitecture are reserved for Intel microprocessors. Please refer to the applicable product User and Reference Guides for more information regarding the specific instruction sets covered by this notice.