THIS IS A TEST INSTANCE. ALL YOUR CHANGES WILL BE LOST!!!!
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Category | Model | Latency batchsize=1 (ms, small is better) | Throughput batchsize=128 (fps, big is better) | ||||
| R1.3 w/ MKL-DNN | master w/ subgraph | speedup | R1.3 w/ MKL-DNN | master w/ subgraph | speedup | ||
CNN/classification | ResNet-50 v1 | ||||||
| ResNet-50 v2 | |||||||
| Inception v3 | |||||||
| Inception v4 | |||||||
| DenseNet | |||||||
| MobileNet | |||||||
| VGG16 | |||||||
| AlexNet | |||||||
| inception-resnet v2 | |||||||
CNN/object detection | Faster R-CNN | ||||||
| SSD-VGG16 | |||||||
| SSD-MobileNet | |||||||
RNN | GNMT | ||||||
| GAN | DCGAN | ||||||
Inference Accuracy
| Inference Accuracy Comparison | ||||||||||
| Alias | Network | # Parameters | Official Results | GPU (with cuDNN) Backend | CPU (without MKL-DNN) | CPU (with MKL-DNN) Backend | ||||
| top1 | top5 | top1 | top5 | top1 | top5 | Top-1 Accuracy | Top-5 Accuracy | |||
| alexnet | AlexNet | 61,100,840 | 0.5492 | 0.7803 | ||||||
| densenet121 | DenseNet-121 | 8,062,504 | 0.7497 | 0.9225 | ||||||
| densenet161 | DenseNet-161 | 28,900,936 | 0.777 | 0.938 | ||||||
| densenet169 | DenseNet-169 | 14,307,880 | 0.7617 | 0.9317 | ||||||
| densenet201 | DenseNet-201 | 20,242,984 | 0.7732 | 0.9362 | ||||||
| inceptionv3 | Inception V3 299x299 | 23,869,000 | 0.7755 | 0.9364 | ||||||
| mobilenet0.25 | MobileNet 0.25 | 475,544 | 0.5185 | 0.7608 | ||||||
| mobilenet0.5 | MobileNet 0.5 | 1,342,536 | 0.6307 | 0.8475 | ||||||
| mobilenet0.75 | MobileNet 0.75 | 2,601,976 | 0.6738 | 0.8782 | ||||||
| mobilenet1.0 | MobileNet 1.0 | 4,253,864 | 0.7105 | 0.9006 | ||||||
| mobilenetv2_1.0 | MobileNetV2 1.0 | 3,539,136 | 0.7192 | 0.9056 | ||||||
| mobilenetv2_0.75 | MobileNetV2 0.75 | 2,653,864 | 0.6961 | 0.8895 | ||||||
| mobilenetv2_0.5 | MobileNetV2 0.5 | 1,983,104 | 0.6449 | 0.8547 | ||||||
| mobilenetv2_0.25 | MobileNetV2 0.25 | 1,526,856 | 0.5074 | 0.7456 | ||||||
| resnet18_v1 | ResNet-18 V1 | 11,699,112 | 0.7093 | 0.8992 | ||||||
| resnet34_v1 | ResNet-34 V1 | 21,814,696 | 0.7437 | 0.9187 | ||||||
| resnet50_v1 | ResNet-50 V1 | 25,629,032 | 0.7647 | 0.9313 | ||||||
| resnet101_v1 | ResNet-101 V1 | 44,695,144 | 0.7834 | 0.9401 | ||||||
| resnet152_v1 | ResNet-152 V1 | 60,404,072 | 0.79 | 0.9438 | ||||||
| resnet18_v2 | ResNet-18 V2 | 11,695,796 | 0.71 | 0.8992 | ||||||
| resnet34_v2 | ResNet-34 V2 | 21,811,380 | 0.744 | 0.9208 | ||||||
| resnet50_v2 | ResNet-50 V2 | 25,595,060 | 0.7711 | 0.9343 | ||||||
| resnet101_v2 | ResNet-101 V2 | 44,639,412 | 0.7853 | 0.9417 | ||||||
| resnet152_v2 | ResNet-152 V2 | 60,329,140 | 0.7921 | 0.9431 | ||||||
| squeezenet1.0 | SqueezeNet 1.0 | 1,248,424 | 0.5611 | 0.7909 | ||||||
| squeezenet1.1 | SqueezeNet 1.1 | 1,235,496 | 0.5496 | 0.7817 | ||||||
| vgg11 | VGG-11 | 132,863,336 | 0.6662 | 0.8734 | ||||||
| vgg13 | VGG-13 | 133,047,848 | 0.6774 | 0.8811 | ||||||
| vgg16 | VGG-16 | 138,357,544 | 0.7323 | 0.9132 | ||||||
| vgg19 | VGG-19 | 143,667,240 | 0.7411 | 0.9135 | ||||||
| vgg11_bn | VGG-11 with batch normalization | 132,874,344 | 0.6859 | 0.8872 | ||||||
| vgg13_bn | VGG-13 with batch normalization | 133,059,624 | 0.6884 | 0.8882 | ||||||
| vgg16_bn | VGG-16 with batch normalization | 138,374,440 | 0.731 | 0.9176 | ||||||
| vgg19_bn | VGG-19 with batch normalization | 143,689,256 | 0.7433 | 0.9185 | ||||||