| Dec 2019 | Custom Resnet 9 Santiago Akle Serrano, Hadi Pour Ansari, Vipul Gupta, Dennis DeCoste source | 0:00:10 | N/A | 94.23% | Tesla V100 * 8 GPU / 32 GB / 40 CPU | Pytorch 1.1.0 |
| Jan 2020 | Custom ResNet 9 Ajay Uppili Arasanipalai source | 0:00:11 | N/A | 94.05% | IBM AC922 + 4 * Nvidia Tesla V100 (NCSA HAL) | PyTorch 1.1.0 |
| Oct 2019 | Kakao Brain Custom ResNet9 clint@KakaoBrain source | 0:00:28 | N/A | 94.04% | Tesla V100 * 4 GPU / 488 GB / 56 CPU (Kakao Brain BrainCloud) | PyTorch 1.1.0 |
| May 2019 | BaiduNet9P Baidu USA GAIT LEOPARD team: Baopu Li, Zhiyu Cheng, Yingze Bao source | 0:00:45 | $0.11 | 94.18% | Baidu Cloud Tesla 8*V100-16GB/448 GB/96 CPU | PyTorch v1.0.1 and PaddlePaddle |
| Oct 2019 | Kakao Brain Custom ResNet9 clint@KakaoBrain source | 0:00:58 | N/A | 94.20% | Tesla V100 * 1 GPU / 488 GB / 56 CPU (Kakao Brain BrainCloud) | PyTorch 1.1.0 |
| May 2019 | BaiduNet9 Baidu USA GAIT LEOPARD team: Baopu Li, Zhiyu Cheng, Yingze Bao source | 0:01:12 | $0.02 | 94.10% | Baidu Cloud Tesla V100*1-16GB/56 GB/12 CPU | PyTorch v1.0.1 and PaddlePaddle |
| Apr 2019 | Custom ResNet 9 Ajay Uppili Arasanipalai source | 0:01:14 | N/A | 94.06% | IBM AC922 + Nvidia Tesla V100 (Nimbix np9g1) | PowerAI 1.6.0 + PyTorch 1.0.1 |
| Nov 2018 | Custom ResNet 9 David Page, myrtle.ai source | 0:01:15 | $0.06 | 94.08% | V100 (AWS p3.2xlarge) | pytorch 0.4.0 |
| Aug 2019 | BaiduNet9 Chuan Li source | 0:01:42 | $0.04 | 94.02% | Lambda GPU Cloud - 4x GTX 1080 Ti | fastai / Pytorch 1.0.0 |
| 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 |
| Apr 2018 | Resnet18 + minor modifications bkj source | 0:05:41 | $0.29 | 94.34% | V100 (AWS p3.2xlarge) | pytorch 0.3.1.post2 |
| 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 |
| 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 |
| 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 |
| 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 (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 |
| 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 (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 (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 | 9:03:29 | $8.76 | 94.91% | 1 K80 / 30 GB / 8 CPU (Google Cloud) | 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 | 9:37:52 | $9.31 | 94.37% | 1 K80 / 30 GB / 8 CPU (Google Cloud) | PyTorch v0.1.12 |
| 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 |
| 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 |
| 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 | 11:00:22 | $10.64 | 94.45% | 1 K80 / 30 GB / 8 CPU (Google Cloud) | TensorFlow v1.2 |
| 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 (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 (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 (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 |