|
Mar 2019
|
ResNet 164 (without bottleneck)
Ryan
source
|
23.3871 |
N/A |
94.10% |
1 P100 / 384 GB / 48 CPU (x86_64 architecture machine) |
TensorFlow v1.2 |
|
Oct 2017
|
ResNet 164 (with bottleneck)
Stanford DAWN
source
|
31.1300 |
$0.05 |
94.58% |
60 GB / 16 CPU (Google Cloud [n1-standard-16]) |
TensorFlow v1.2 |
|
Oct 2017
|
ResNet 164 (with bottleneck)
Stanford DAWN
source
|
44.1859 |
$0.12 |
94.45% |
1 K80 / 30 GB / 8 CPU (Google Cloud) |
TensorFlow v1.2 |
|
Oct 2017
|
ResNet 164 (with bottleneck)
Stanford DAWN
source
|
28.1000 |
N/A |
94.46% |
1 P100 / 512 GB / 56 CPU (DAWN Internal Cluster) |
TensorFlow v1.2 |
|
Nov 2018
|
Custom ResNet 9 using PyTorch JIT in C++
Laurent Mazare
source
|
0.8280 |
N/A |
94.53% |
1 P100 / 128 GB / 16 CPU |
PyTorch v1.0.0.dev20181116 |
|
Oct 2017
|
ResNet 164 (with bottleneck)
Stanford DAWN
source
|
75.3522 |
$0.11 |
95.01% |
60 GB / 16 CPU (Google Cloud [n1-standard-16]) |
PyTorch v0.1.12 |
|
Oct 2017
|
ResNet 164 (without bottleneck)
Stanford DAWN
source
|
25.2188 |
N/A |
94.46% |
1 P100 / 512 GB / 56 CPU (DAWN Internal Cluster) |
PyTorch v0.1.12 |
|
Oct 2017
|
ResNet 164 (without bottleneck)
Stanford DAWN
source
|
28.3201 |
$0.07 |
94.49% |
1 K80 / 61 GB / 4 CPU (Amazon EC2 [p2.xlarge]) |
PyTorch v0.1.12 |
|
Oct 2017
|
ResNet 164 (with bottleneck)
Stanford DAWN
source
|
38.5826 |
$0.10 |
94.58% |
1 K80 / 61 GB / 4 CPU (Amazon EC2 [p2.xlarge]) |
TensorFlow v1.2 |
|
Oct 2017
|
ResNet 164 (with bottleneck)
Stanford DAWN
source
|
31.3490 |
$0.08 |
94.94% |
1 K80 / 30 GB / 8 CPU (Google Cloud) |
PyTorch v0.1.12 |
|
Oct 2017
|
ResNet 56
Stanford DAWN
source
|
9.7843 |
$0.02 |
94.09% |
1 K80 / 61 GB / 4 CPU (Amazon EC2 [p2.xlarge]) |
PyTorch v0.1.12 |
|
Nov 2019
|
ResNet8
ModelArts Service of Huawei Cloud
source
|
0.1345 |
N/A |
94.20% |
Huawei Cloud [pi2.2xlarge.4] |
ModelArts-AIBOX + TensorRT |
|
Oct 2017
|
ResNet 164 (with bottleneck)
Stanford DAWN
source
|
24.6291 |
N/A |
94.97% |
1 P100 / 512 GB / 56 CPU (DAWN Internal Cluster) |
PyTorch v0.1.12 |
|
Oct 2017
|
ResNet 164 (without bottleneck)
Stanford DAWN
source
|
85.8511 |
$0.13 |
94.48% |
60 GB / 16 CPU (Google Cloud [n1-standard-16]) |
PyTorch v0.1.12 |
|
Oct 2017
|
ResNet 164 (with bottleneck)
Stanford DAWN
source
|
28.6880 |
$0.07 |
94.97% |
1 K80 / 61 GB / 4 CPU (Amazon EC2 [p2.xlarge]) |
PyTorch v0.1.12 |
|
Apr 2019
|
BaiduNet8 using PyTorch JIT in C++
Baidu USA GAIT LEOPARD team: Baopu Li, Zhiyu Cheng, Jiazhuo Wang, Haofeng Kou, Yingze Bao
source
|
0.6830 |
$0.00 |
94.32% |
Baidu Cloud Tesla V100*1/60 GB/12 CPU |
PyTorch v1.0.1 and PaddlePaddle |
|
Oct 2017
|
ResNet 164 (without bottleneck)
Stanford DAWN
source
|
24.9200 |
$0.04 |
94.04% |
60 GB / 16 CPU (Google Cloud [n1-standard-16]) |
TensorFlow v1.2 |
|
Oct 2017
|
ResNet 164 (without bottleneck)
Stanford DAWN
source
|
31.7121 |
$0.09 |
94.39% |
1 K80 / 30 GB / 8 CPU (Google Cloud) |
PyTorch v0.1.12 |
|
Oct 2017
|
ResNet 164 (without bottleneck)
Stanford DAWN
source
|
35.4519 |
N/A |
94.19% |
1 P100 / 512 GB / 56 CPU (DAWN Internal Cluster) |
TensorFlow v1.2 |
|
Oct 2019
|
Kakao Brain Custom ResNet9 using PyTorch JIT in python
clint@KakaoBrain
source
|
0.8570 |
N/A |
94.23% |
Tesla V100 * 1 GPU / 488 GB / 56 CPU (Kakao Brain BrainCloud) |
PyTorch 1.1.0 |
|
Oct 2017
|
ResNet 164 (without bottleneck)
Stanford DAWN
source
|
58.9259 |
$0.16 |
94.31% |
1 K80 / 30 GB / 8 CPU (Google Cloud) |
TensorFlow v1.2 |