THIS IS A TEST INSTANCE. ALL YOUR CHANGES WILL BE LOST!!!!
...
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
The model is from gluon model zoo by pre-trained parameters. The top1 and top5 accuracy are verified by MKL-DNN backend.
Inference Accuracy Comparison | ||||||||
Alias | Network | # Parameters | GPU (with cuDNN) Backend | CPU (without MKL-DNN) | CPU (with MKL-DNN) Backend | |||
top1 | top5 | top1 | top5 | top1 | top5 | |||
alexnet | AlexNet | 61,100,840 | ||||||
densenet121 | DenseNet-121 | 8,062,504 | ||||||
densenet161 | DenseNet-161 | 28,900,936 | ||||||
densenet169 | DenseNet-169 | 14,307,880 | ||||||
densenet201 | DenseNet-201 | 20,242,984 | ||||||
inceptionv3 | Inception V3 299x299 | 23,869,000 | ||||||
mobilenet0.25 | MobileNet 0.25 | 475,544 | ||||||
mobilenet0.5 | MobileNet 0.5 | 1,342,536 | ||||||
mobilenet0.75 | MobileNet 0.75 | 2,601,976 | ||||||
mobilenet1.0 | MobileNet 1.0 | 4,253,864 | ||||||
mobilenetv2_1.0 | MobileNetV2 1.0 | 3,539,136 | ||||||
mobilenetv2_0.75 | MobileNetV2 0.75 | 2,653,864 | ||||||
mobilenetv2_0.5 | MobileNetV2 0.5 | 1,983,104 | ||||||
mobilenetv2_0.25 | MobileNetV2 0.25 | 1,526,856 | ||||||
resnet18_v1 | ResNet-18 V1 | 11,699,112 | ||||||
resnet34_v1 | ResNet-34 V1 | 21,814,696 | ||||||
resnet50_v1 | ResNet-50 V1 | 25,629,032 | ||||||
resnet101_v1 | ResNet-101 V1 | 44,695,144 | ||||||
resnet152_v1 | ResNet-152 V1 | 60,404,072 | ||||||
resnet18_v2 | ResNet-18 V2 | 11,695,796 | ||||||
resnet34_v2 | ResNet-34 V2 | 21,811,380 | ||||||
resnet50_v2 | ResNet-50 V2 | 25,595,060 | ||||||
resnet101_v2 | ResNet-101 V2 | 44,639,412 | ||||||
resnet152_v2 | ResNet-152 V2 | 60,329,140 | ||||||
squeezenet1.0 | SqueezeNet 1.0 | 1,248,424 | ||||||
squeezenet1.1 | SqueezeNet 1.1 | 1,235,496 | ||||||
vgg11 | VGG-11 | 132,863,336 | ||||||
vgg13 | VGG-13 | 133,047,848 | ||||||
vgg16 | VGG-16 | 138,357,544 | ||||||
vgg19 | VGG-19 | 143,667,240 | ||||||
vgg11_bn | VGG-11 with batch normalization | 132,874,344 | ||||||
vgg13_bn | VGG-13 with batch normalization | 133,059,624 | ||||||
vgg16_bn | VGG-16 with batch normalization | 138,374,440 | ||||||
vgg19_bn | VGG-19 with batch normalization | 143,689,256 |
...