From 0a27e6a00eab472ac94cd6f397ad1380cc2f52d8 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Dr=2E=20Artificial=E6=9B=BE=E5=B0=8F=E5=81=A5?=
 <875100501@qq.com>
Date: Mon, 5 Aug 2024 20:45:42 +0800
Subject: [PATCH] fix the wrong words

fix the wrong words
---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 7923e211..6907c20d 100644
--- a/README.md
+++ b/README.md
@@ -83,7 +83,7 @@ NLVR2 | <a href="https://storage.googleapis.com/sfr-vision-language-research/BLI
 In order to finetune a model with ViT-L, simply change the config file to set 'vit' as large. Batch size and learning rate may also need to be adjusted accordingly (please see the paper's appendix for hyper-parameter details). <a href="https://github.com/facebookresearch/fairscale">Gradient checkpoint</a> can also be activated in the config file to reduce GPU memory usage. 
 
 ### Pre-train:
-1. Prepare training json files where each json file contains a list. Each item in the list is a dictonary with two key-value pairs: {'image': path_of_image, 'caption': text_of_image}. 
+1. Prepare training json files where each json file contains a list. Each item in the list is a dictionary with two key-value pairs: {'image': path_of_image, 'caption': text_of_image}. 
 2. In configs/pretrain.yaml, set 'train_file' as the paths for the json files .
 3. Pre-train the model using 8 A100 GPUs:
 <pre>python -m torch.distributed.run --nproc_per_node=8 pretrain.py --config ./configs/Pretrain.yaml --output_dir output/Pretrain </pre>