diff --git a/modelcenter/PP-HelixFold/benchmark_en.md b/modelcenter/PP-HelixFold/benchmark_en.md
index 985ef63d12ff2a822d895434e1c2c6ab4b7183c1..11835fdd6c25def77a8ce0543950127c434f245f 100644
--- a/modelcenter/PP-HelixFold/benchmark_en.md
+++ b/modelcenter/PP-HelixFold/benchmark_en.md
@@ -4,18 +4,18 @@
 The PP-Helixfold model's inference test is tested on single-card NVIDIA A100 (40G), batch size=1. To reproduce the results reported in our paper, specific environment settings are required as below.
 
 * Python: 3.7
-* CUDA 11.2
-* CUDNN 8.10.1
-* NCCL 2.12.12.
+* CUDA 11.6
+* CUDNN 8.4.0
+* NCCL 2.14.3
 
 ### 1.2 Datasets
-For training, the PP-Helixfold model uses 25% of samples from RCSB PDB and 75% of self-distillation samples. For evaluation, we collect 87 domain targets from CASP14 and 371 protein targets from CAMEO, ranging from 2021-09-04 to 2022-02-19.
+For training, the PP-Helixfold model uses 25% of samples from RCSB PDB and 75% of self-distillation samples. For evaluation, we collect 87 domain targets from CASP14 and 60 protein targets from CAMEO, ranging from 2022-08-01 to 2022-08-31.
 
 ### 1.3 Performance
-Compared with the computational performance of AlphaFold2 reported in the paper and OpenFold implemented through PyTorch, PP-Helixfold reduces the training time from about 11 days to 7.5 days, and it can be further reduced to only 5.3 days when using hybrid parallelism. Training PP-Helixfold from scratch can achieve competitive accuracy with AlphaFold2.
+Compared with the computational performance of AlphaFold2 reported in the paper and OpenFold implemented through PyTorch, PP-Helixfold reduces the training time from about 11 days to 5.12 days, and it can be further reduced to only 2.89 days when using hybrid parallelism. Training PP-Helixfold from scratch can achieve competitive accuracy with AlphaFold2.
 
-![](https://github.com/PaddlePaddle/PaddleHelix/blob/dev/.github/HelixFold_computational_performance.png)
-![](https://github.com/PaddlePaddle/PaddleHelix/blob/dev/.github/HelixFold_accuracy.png)
+![](https://github.com/PaddlePaddle/PaddleHelix/blob/dev/.github/HelixFold_computational_perf.png)
+![](https://github.com/PaddlePaddle/PaddleHelix/blob/dev/.github/HelixFold_infer_accuracy.png)
 
 
 ## 2. Reference