|
Oct 2017
|
ResNet 164 (with bottleneck)
Stanford DAWN
source
|
4 days, 6:48:08
|
$54.79 |
94.58% |
60 GB / 16 CPU (Google Cloud [n1-standard-16]) |
TensorFlow v1.2 |
|
Oct 2017
|
ResNet 164 (with bottleneck)
Stanford DAWN
source
|
11:00:22
|
$10.64 |
94.45% |
1 K80 / 30 GB / 8 CPU (Google Cloud) |
TensorFlow v1.2 |
|
Oct 2017
|
ResNet 164 (with bottleneck)
Stanford DAWN
source
|
3:29:30
|
N/A |
94.46% |
1 P100 / 512 GB / 56 CPU (DAWN Internal Cluster) |
TensorFlow v1.2 |
|
Oct 2017
|
ResNet 164 (with bottleneck)
Stanford DAWN
source
|
4 days, 1:10:39
|
$51.80 |
94.79% |
60 GB / 16 CPU (Google Cloud [n1-standard-16]) |
PyTorch v0.1.12 |
|
Oct 2017
|
ResNet 164 (without bottleneck)
Stanford DAWN
source
|
2:31:42
|
N/A |
94.46% |
1 P100 / 512 GB / 56 CPU (DAWN Internal Cluster) |
PyTorch v0.1.12 |
|
Apr 2018
|
Custom Wide Resnet
fast.ai + students team: Jeremy Howard, Andrew Shaw, Brett Koonce, Sylvain Gugger
source
|
0:06:45
|
$0.26 |
94.20% |
Paperspace Volta (V100) |
fastai / pytorch |
|
Feb 2018
|
ResNet 164 (without bottleneck)
Stanford DAWN
source
|
2:47:50
|
N/A |
94.18% |
1 P100 / 512 GB / 56 CPU (DAWN Internal Cluster) |
TensorFlow v1.3 |
|
Oct 2017
|
ResNet 164 (without bottleneck)
Stanford DAWN
source
|
10:32:14
|
$9.48 |
94.32% |
1 K80 / 61 GB / 4 CPU (Amazon EC2 [p2.xlarge]) |
PyTorch v0.1.12 |
|
Apr 2018
|
KervResNet34
Chen Wang
source
|
0:35:37
|
N/A |
95.29% |
1 GPU (Nvidia GeForce GTX 1080 Ti) |
PyTorch 0.3.1 |
|
Jan 2018
|
ResNet50
DIUX
source
|
1:07:55
|
$3.46 |
94.60% |
p3.2xlarge |
tensorflow 1.5, tensorpack 0.8.1 |
|
Apr 2018
|
Resnet18 + minor modifications
bkj
source
|
0:05:41
|
$0.29 |
94.34% |
V100 (AWS p3.2xlarge) |
pytorch 0.3.1.post2 |
|
Oct 2017
|
ResNet 164 (with bottleneck)
Stanford DAWN
source
|
10:53:08
|
$9.80 |
94.58% |
1 K80 / 61 GB / 4 CPU (Amazon EC2 [p2.xlarge]) |
TensorFlow v1.2 |
|
Oct 2017
|
ResNet 164 (with bottleneck)
Stanford DAWN
source
|
9:03:29
|
$8.76 |
94.91% |
1 K80 / 30 GB / 8 CPU (Google Cloud) |
PyTorch v0.1.12 |
|
Jan 2018
|
ResNet50
DIUX
source
|
3:18:50
|
$3.78 |
94.51% |
g3.4xlarge |
tensorflow 1.5, tensorpack 0.8.1 |
|
Oct 2017
|
ResNet 164 (with bottleneck)
Stanford DAWN
source
|
3:01:52
|
N/A |
94.82% |
1 P100 / 512 GB / 56 CPU (DAWN Internal Cluster) |
PyTorch v0.1.12 |
|
Oct 2017
|
ResNet 164 (without bottleneck)
Stanford DAWN
source
|
3 days, 7:26:54
|
$42.35 |
94.37% |
60 GB / 16 CPU (Google Cloud [n1-standard-16]) |
PyTorch v0.1.12 |
|
Oct 2017
|
ResNet 164 (with bottleneck)
Stanford DAWN
source
|
9:16:32
|
$8.35 |
94.61% |
1 K80 / 61 GB / 4 CPU (Amazon EC2 [p2.xlarge]) |
PyTorch v0.1.12 |
|
Oct 2017
|
ResNet 164 (without bottleneck)
Stanford DAWN
source
|
3 days, 22:09:47
|
$50.19 |
94.04% |
60 GB / 16 CPU (Google Cloud [n1-standard-16]) |
TensorFlow v1.2 |
|
Oct 2017
|
ResNet 164 (without bottleneck)
Stanford DAWN
source
|
9:37:52
|
$9.31 |
94.37% |
1 K80 / 30 GB / 8 CPU (Google Cloud) |
PyTorch v0.1.12 |
|
Apr 2018
|
Custom Wide Resnet
fast.ai + students team: Jeremy Howard, Andrew Shaw, Brett Koonce, Sylvain Gugger
source
|
0:02:54
|
$1.18 |
94.39% |
8 * V100 (AWS p3.16xlarge) |
fastai / pytorch |
|
Oct 2017
|
ResNet 164 (without bottleneck)
Stanford DAWN
source
|
3:20:27
|
N/A |
94.19% |
1 P100 / 512 GB / 56 CPU (DAWN Internal Cluster) |
TensorFlow v1.2 |
|
Oct 2017
|
ResNet 164 (without bottleneck)
Stanford DAWN
source
|
10:02:45
|
$9.71 |
94.31% |
1 K80 / 30 GB / 8 CPU (Google Cloud) |
TensorFlow v1.2 |
