From 9d0924a4ce1b9b81ea761fba9a2f5c023e3ca133 Mon Sep 17 00:00:00 2001
From: HantingChen <40243544+HantingChen@users.noreply.github.com>
Date: Mon, 31 Jul 2023 17:58:11 +0800
Subject: [PATCH] Update README.md

---
 README.md | 34 +++++++++++++++++-----------------
 1 file changed, 17 insertions(+), 17 deletions(-)

diff --git a/README.md b/README.md
index 77f2ae7..8a056e3 100644
--- a/README.md
+++ b/README.md
@@ -27,24 +27,24 @@ VanillaNet achieves comparable performance to prevalent computer vision foundati
 - **13 layers'** VanillaNet (1.5x*) achieves about **83%** Top-1 accuracy with **9.72ms**, over **100%** speed increase compared to Swin-S (**20.25ms**).
 - With tensorRT FP32 on A100, **11 layers'** VanillaNet achieves about **81%** Top-1 accuracy with **0.69ms**, over **100%** speed increase compared to Swin-T (**1.41ms**) and ResNet-101 (**1.58ms**).
 
-| name | Params(M) | FLOPs(B) | Acc(%) | Latency(ms) <br/>Pytorch <br/>A100 | Latency(ms) <br/>MindSpore <br/>Ascend 910 | Latency(ms) <br/>TRT FP32 <br/>A100 | Latency(ms) <br/>TRT FP16 <br/>A100 |  Latency(ms) <br/>TRT FP16 <br/>V100 |
+| name | Params(M) | FLOPs(B) | Acc(%) | Latency(ms) <br/>Pytorch <br/>A100 | Latency(ms) <br/>MindSpore <br/>Ascend 910 | Latency(ms) <br/>TRT FP32 <br/>A100 | Latency(ms) <br/>TRT FP16 <br/>A100 |  Latency(ms) <br/>TRT FP32 <br/>V100 |
 |:---:|:---:|:---:|:---:| :---:|:---:|:---:|:---:|:---:|
-| Swin-T | 28.3 | 4.5 | 81.18 | 10.51 | 2.24 | 1.41 | 0.98 | 2.83 |
-| ConvNextV2-N | 15.6 | 2.45 | 81.2 | 6.85 | 3.43 | - | - | 2.27 |
-| ResNet-18 | 11.7 | 1.8 | 70.6 | 3.12 | 0.60 | 0.41 | 0.28 | 0.55 |
-| ResNet-34 |21.8|3.7|75.5|5.57|0.97|0.77|0.49| 0.95 |
-| ResNet-50 |25.6|4.1|79.8|7.64|1.23|0.80|0.54| 1.24 |
-| ResNet-101 |45.0|8.0|81.3|15.35|2.34|1.58|1.04| 1.86 |
-| ResNet-152 |60.2|11.5|81.8|22.19|3.40|2.30|1.49| 3.06 |
-| **VanillaNet-5** | 15.5 | 5.2 | 72.49 | 1.61 |0.39|0.33|0.27| 0.61 |
-| **VanillaNet-6** | 32.5 | 6.0 | 76.36 | 2.01 |0.53|0.40|0.33|0.66|
-| **VanillaNet-7** | 32.8 | 6.9 | 77.98 | 2.27 |0.76|0.47|0.39|0.71|
-| **VanillaNet-8** | 37.1 | 7.7 | 79.13 | 2.56 |0.80|0.52|0.45|0.79|
-| **VanillaNet-9** | 41.4 | 8.6 | 79.87 | 2.91 |0.86|0.58|0.49|0.91|
-| **VanillaNet-10** | 45.7 | 9.4 | 80.57 | 3.24 |0.89|0.63|0.53|1.01|
-| **VanillaNet-11** | 50.0 | 10.3 | 81.08 | 3.59 | 0.95 |0.69|0.58|1.20|
-| **VanillaNet-12** | 54.3 | 11.1 | 81.55 | 3.82 |1.00|0.75|0.62|1.38|
-| **VanillaNet-13** | 58.6 | 11.9 | 82.05 | 4.26 |1.05|0.82|0.67|1.39|
+| Swin-T | 28.3 | 4.5 | 81.18 | 10.51 | 2.24 | 1.41 | 0.98 |4.71 |
+| ConvNextV2-N | 15.6 | 2.45 | 81.2 | 6.85 | 3.43 | - | - | 2.81 |
+| ResNet-18 | 11.7 | 1.8 | 70.6 | 3.12 | 0.60 | 0.41 | 0.28 | 1.63 |
+| ResNet-34 |21.8|3.7|75.5|5.57|0.97|0.77|0.49| 2.18 |
+| ResNet-50 |25.6|4.1|79.8|7.64|1.23|0.80|0.54| 3.03 |
+| ResNet-101 |45.0|8.0|81.3|15.35|2.34|1.58|1.04| 4.46 |
+| ResNet-152 |60.2|11.5|81.8|22.19|3.40|2.30|1.49| 6.89 |
+| **VanillaNet-5** | 15.5 | 5.2 | 72.49 | 1.61 |0.39|0.33|0.27| 1.60 |
+| **VanillaNet-6** | 32.5 | 6.0 | 76.36 | 2.01 |0.53|0.40|0.33|1.84|
+| **VanillaNet-7** | 32.8 | 6.9 | 77.98 | 2.27 |0.76|0.47|0.39|2.02|
+| **VanillaNet-8** | 37.1 | 7.7 | 79.13 | 2.56 |0.80|0.52|0.45|2.26|
+| **VanillaNet-9** | 41.4 | 8.6 | 79.87 | 2.91 |0.86|0.58|0.49|2.55|
+| **VanillaNet-10** | 45.7 | 9.4 | 80.57 | 3.24 |0.89|0.63|0.53|2.84|
+| **VanillaNet-11** | 50.0 | 10.3 | 81.08 | 3.59 | 0.95 |0.69|0.58|3.14|
+| **VanillaNet-12** | 54.3 | 11.1 | 81.55 | 3.82 |1.00|0.75|0.62|3.44|
+| **VanillaNet-13** | 58.6 | 11.9 | 82.05 | 4.26 |1.05|0.82|0.67|3.69|
 
 ## Downstream Tasks