Navigation : MXNet AlexNet CIFAR_WideResNet16_10 CIFAR_WideResNet28_10 CIFAR_WideResNet40_8 DenseNet121 DenseNet161 Faster_RCNN_ResNet50_v1b_VOC MobileNet_0.25 MobileNet_0.5 MobileNet_0.75 MobileNet_1.0 MobileNet_v2_0.25 MobileNet_v2_0.5 MobileNet_v2_0.75 MobileNet_v2_1.0 ResNet101_v1 ResNet101_v2 ResNet152_v1 ResNet152_v2 ResNet18_v1 ResNet18_v2 ResNet34_v1 ResNet34_v2 ResNet50_v1 ResNet50_v2 ResNext101_32x4d ResNext50_32x4d SSD_300_VGG16_Atrous_COCO SSD_512_ResNet50_v1_COCO SSD_512_ResNet50_v1_VOC SSD_512_VGG16_Atrous_COCO SqueezeNet_v1.0 SqueezeNet_v1.1 VGG16 VGG19 TensorFlow AI_Matrix Densenet121 AI_Matrix_GoogleNet AI_Matrix_ResNet152 AI_Matrix_ResNet50 BVLC_AlexNet_Caffe BVLC_GoogLeNet_Caffe EC2_P3_CPU (E5-2686 v4) Quadro_RTX_6000 Batch Size = 1 Model Information Model Latency and Throughput Tesla_K80 Tesla_M60 Tesla_P100_PCIE_16GB Tesla_P4 Tesla_V100_SXM2_16GB DeepLabv3_MobileNet_v2_DM_05_PASCAL_VOC_Train_Val DeepLabv3_PASCAL_VOC_Train_Val Faster_RCNN_Inception_v2_COCO Faster_RCNN_NAS_COCO Faster_RCNN_ResNet101_COCO Faster_RCNN_ResNet50_COCO Inception_5h Inception_ResNet_v2 Inception_v1 Inception_v2 Inception_v3 Inception_v4 MLPerf_Mobilenet_v1 MLPerf_ResNet50_v1.5 MLPerf_SSD_MobileNet_v1_300x300 MLPerf_SSD_ResNet34_1200x1200 Mask_RCNN_Inception_ResNet_v2_Atrous_COCO Mask_RCNN_Inception_v2_COCO Mask_RCNN_ResNet101_v2_Atrous_COCO Mask_RCNN_ResNet50_v2_Atrous_COCO MobileNet_v1_0.25_128 MobileNet_v1_0.25_160 MobileNet_v1_0.25_192 MobileNet_v1_0.25_224 MobileNet_v1_0.5_128 MobileNet_v1_0.5_160 MobileNet_v1_0.5_192 MobileNet_v1_0.5_224 MobileNet_v1_0.75_128 MobileNet_v1_0.75_160 MobileNet_v1_0.75_192 MobileNet_v1_0.75_224 MobileNet_v1_1.0_128 MobileNet_v1_1.0_160 MobileNet_v1_1.0_192 MobileNet_v1_1.0_224 ResNet_v1_101 ResNet_v1_152 ResNet_v1_50 ResNet_v2_101 ResNet_v2_152 ResNet_v2_50 SRGAN SSD_Inception_v2_COCO SSD_MobileNet_v1_0.75_Depth_300x300_COCO14_Sync SSD_MobileNet_v1_COCO SSD_MobileNet_v1_FPN_Shared_Box_Predictor_640x640_COCO14_Sync SSD_MobileNet_v1_PPN_Shared_Box_Predictor_300x300_COCO14_Sync SSD_MobileNet_v2_COCO VGG16 VGG19Quadro_RTX_6000TensorFlow BVLC_GoogLeNet_Caffe from AI-Matrix Batch Size = 1 on Quadro_RTX_6000Model InformationModel Latency and ThroughputBatch Size = 1GPU Kernel Information Aggregated by Name Model Information