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@@ -60,14 +60,14 @@ NLVR2 | <a href="https://storage.googleapis.com/sfr-vision-language-research/BLI
 <pre>python -m torch.distributed.run --nproc_per_node=8 train_caption.py --evaluate</pre> 
 3. To evaluate the finetuned BLIP model on NoCaps, generate results with: (evaluation needs to be performed on official server)
 <pre>python -m torch.distributed.run --nproc_per_node=8 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:
+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_capfilt_large.pth". Then run:
 <pre>python -m torch.distributed.run --nproc_per_node=8 train_caption.py </pre> 
 
 ### VQA:
 1. Download VQA v2 dataset and Visual Genome dataset from the original websites, and set 'vqa_root' and 'vg_root' in configs/vqa.yaml.
 2. To evaluate the finetuned BLIP model, generate results with: (evaluation needs to be performed on official server)
 <pre>python -m torch.distributed.run --nproc_per_node=8 train_vqa.py --evaluate</pre> 
-3. To finetune the pre-trained checkpoint using 16 A100 GPUs, first set 'pretrained' in configs/vqa.yaml as "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model*_base.pth". Then run:
+3. To finetune the pre-trained checkpoint using 16 A100 GPUs, first set 'pretrained' in configs/vqa.yaml as "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth". Then run:
 <pre>python -m torch.distributed.run --nproc_per_node=16 train_vqa.py </pre> 
 
 ### NLVR2: