diff --git a/README.md b/README.md
index 9e316cd..7650a23 100644
--- a/README.md
+++ b/README.md
@@ -31,13 +31,23 @@ NLVR2 | <a href="https://storage.googleapis.com/sfr-vision-language-research/BLI
 
 
 ### Image-Text Retrieval:
-1. Download COCO or Flickr30k datasets from the original websites, and set 'image_root' in configs/retrieval_{dataset}.yaml accordingly.
+1. Download COCO and Flickr30k datasets from the original websites, and set 'image_root' in configs/retrieval_{dataset}.yaml accordingly.
 2. To evaluate the finetuned BLIP model on COCO, run:
 <pre>python -m torch.distributed.run --nproc_per_node=8 --use_env train_retrieval.py \
 --config ./configs/retrieval_coco.yaml \
 --output_dir output/retrieval_coco \
 --evaluate</pre> 
-3. To finetune the pre-trained checkpoint using 8 A100 GPUs, first set 'pretrained' in configs/retrieval_coco.yaml as . Then run:
+3. To finetune the pre-trained checkpoint using 8 A100 GPUs, first set 'pretrained' in configs/retrieval_coco.yaml as "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base.pth". Then run:
 <pre>python -m torch.distributed.run --nproc_per_node=8 --use_env train_retrieval.py \
 --config ./configs/retrieval_coco.yaml \
 --output_dir output/retrieval_coco </pre> 
+
+### Image-Text Captioning:
+1. Download COCO and NoCaps datasets from the original websites, and set 'image_root' in configs/caption_coco.yaml and configs/nocaps.yaml accordingly.
+2. To evaluate the finetuned BLIP model on COCO, run:
+<pre>python -m torch.distributed.run --nproc_per_node=8 --use_env train_caption.py --evaluate</pre> 
+3. To evaluate the finetuned BLIP model on NoCaps, generate results with:
+<pre>python -m torch.distributed.run --nproc_per_node=8 --use_env eval_nocaps.py </pre> 
+4. To finetune the pre-trained checkpoint using 8 A100 GPUs, first set 'pretrained' in configs/caption_coco.yaml as "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model*_base.pth". Then run:
+<pre>python -m torch.distributed.run --nproc_per_node=8 --use_env train_caption.py </pre> 
+