diff --git a/configs/retrieval_msrvtt.yaml b/configs/retrieval_msrvtt.yaml
new file mode 100644
index 0000000..395f625
--- /dev/null
+++ b/configs/retrieval_msrvtt.yaml
@@ -0,0 +1,12 @@
+video_root: '/export/share/dongxuli/data/msrvtt_retrieval/videos'
+ann_root: 'annotation'
+
+# set pretrained as a file path or an url
+pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'
+
+# size of vit model; base or large
+vit: 'base'
+batch_size: 64
+k_test: 128
+image_size: 384
+num_frm_test: 8
\ No newline at end of file
diff --git a/data/video_dataset.py b/data/video_dataset.py
new file mode 100644
index 0000000..0a6f8a6
--- /dev/null
+++ b/data/video_dataset.py
@@ -0,0 +1,110 @@
+from torch.utils.data import Dataset
+from torchvision.datasets.utils import download_url
+
+from PIL import Image
+import torch
+import numpy as np
+import random
+import decord
+from decord import VideoReader
+import json
+import os
+from data.utils import pre_caption
+
+decord.bridge.set_bridge("torch")
+
+class ImageNorm(object):
+    """Apply Normalization to Image Pixels on GPU
+    """
+    def __init__(self, mean, std):
+        self.mean = torch.tensor(mean).view(1, 3, 1, 1)
+        self.std = torch.tensor(std).view(1, 3, 1, 1)
+        
+    def __call__(self, img):
+
+        if torch.max(img) > 1 and self.mean.max() <= 1:
+            img.div_(255.)
+        return img.sub_(self.mean).div_(self.std)
+
+def load_jsonl(filename):
+    with open(filename, "r") as f:
+        return [json.loads(l.strip("\n")) for l in f.readlines()]
+    
+    
+class VideoDataset(Dataset):
+
+    def __init__(self, video_root, ann_root, num_frm=4, frm_sampling_strategy="rand", max_img_size=384, video_fmt='.mp4'):
+        '''
+        image_root (string): Root directory of video
+        ann_root (string): directory to store the annotation file
+        '''        
+        url = 'https://storage.googleapis.com/sfr-vision-language-research/datasets/msrvtt_test.jsonl'
+        filename = 'msrvtt_test.jsonl'
+
+        download_url(url,ann_root)
+        self.annotation = load_jsonl(os.path.join(ann_root,filename))
+        
+        self.num_frm = num_frm
+        self.frm_sampling_strategy = frm_sampling_strategy
+        self.max_img_size = max_img_size
+        self.video_root = video_root
+        self.video_fmt = video_fmt
+        self.img_norm = ImageNorm(mean=(0.48145466, 0.4578275, 0.40821073), std=(0.26862954, 0.26130258, 0.27577711))
+
+        self.text = [pre_caption(ann['caption'],40) for ann in self.annotation]
+        self.txt2video = [i for i in range(len(self.annotation))]
+        self.video2txt = self.txt2video               
+            
+            
+    def __len__(self):
+        return len(self.annotation)
+
+    def __getitem__(self, index):
+
+        ann = self.annotation[index]  
+
+        video_path = os.path.join(self.video_root, ann['clip_name'] + self.video_fmt) 
+
+        vid_frm_array = self._load_video_from_path_decord(video_path, height=self.max_img_size, width=self.max_img_size)
+
+        video = self.img_norm(vid_frm_array.float())
+        
+        return video, ann['clip_name']
+
+        
+
+    def _load_video_from_path_decord(self, video_path, height=None, width=None, start_time=None, end_time=None, fps=-1):
+        try:
+            if not height or not width:
+                vr = VideoReader(video_path)
+            else:
+                vr = VideoReader(video_path, width=width, height=height)
+
+            vlen = len(vr)
+
+            if start_time or end_time:
+                assert fps > 0, 'must provide video fps if specifying start and end time.'
+
+                start_idx = min(int(start_time * fps), vlen)
+                end_idx = min(int(end_time * fps), vlen)
+            else:
+                start_idx, end_idx = 0, vlen
+
+            if self.frm_sampling_strategy == 'uniform':
+                frame_indices = np.arange(start_idx, end_idx, vlen / self.num_frm, dtype=int)
+            elif self.frm_sampling_strategy == 'rand':
+                frame_indices = sorted(random.sample(range(vlen), self.num_frm))
+            elif self.frm_sampling_strategy == 'headtail':
+                frame_indices_head = sorted(random.sample(range(vlen // 2), self.num_frm // 2))
+                frame_indices_tail = sorted(random.sample(range(vlen // 2, vlen), self.num_frm // 2))
+                frame_indices = frame_indices_head + frame_indices_tail
+            else:
+                raise NotImplementedError('Invalid sampling strategy {} '.format(self.frm_sampling_strategy))
+
+            raw_sample_frms = vr.get_batch(frame_indices)
+        except Exception as e:
+            return None
+
+        raw_sample_frms = raw_sample_frms.permute(0, 3, 1, 2)
+
+        return raw_sample_frms
diff --git a/eval_retrieval_video.py b/eval_retrieval_video.py
new file mode 100644
index 0000000..07ebab7
--- /dev/null
+++ b/eval_retrieval_video.py
@@ -0,0 +1,250 @@
+'''
+ * Copyright (c) 2022, salesforce.com, inc.
+ * All rights reserved.
+ * SPDX-License-Identifier: BSD-3-Clause
+ * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+ * By Junnan Li
+'''
+import argparse
+import os
+import ruamel_yaml as yaml
+import numpy as np
+import random
+import time
+import datetime
+import json
+from pathlib import Path
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.backends.cudnn as cudnn
+import torch.distributed as dist
+from torch.utils.data import DataLoader
+
+from models.blip_retrieval import blip_retrieval
+import utils
+from data.video_dataset import VideoDataset
+
+
+@torch.no_grad()
+def evaluation(model, data_loader, tokenizer, device, config):
+    # test
+    model.eval() 
+    
+    metric_logger = utils.MetricLogger(delimiter="  ")
+    header = 'Evaluation:'    
+    
+    print('Computing features for evaluation...')
+    start_time = time.time()  
+
+    texts = data_loader.dataset.text   
+    num_text = len(texts)
+    text_bs = 256
+    text_ids = []
+    text_embeds = []  
+    text_atts = []
+    for i in range(0, num_text, text_bs):
+        text = texts[i: min(num_text, i+text_bs)]
+        text_input = tokenizer(text, padding='max_length', truncation=True, max_length=35, return_tensors="pt").to(device) 
+        text_output = model.text_encoder(text_input.input_ids, attention_mask = text_input.attention_mask, mode='text')  
+        text_embed = F.normalize(model.text_proj(text_output.last_hidden_state[:,0,:]))
+        text_embeds.append(text_embed)   
+        text_ids.append(text_input.input_ids)
+        text_atts.append(text_input.attention_mask)
+    
+    text_embeds = torch.cat(text_embeds,dim=0)
+    text_ids = torch.cat(text_ids,dim=0)
+    text_atts = torch.cat(text_atts,dim=0)
+    text_ids[:,0] = tokenizer.additional_special_tokens_ids[0]
+    
+    video_feats = []
+    video_embeds = []
+    for video, video_id in data_loader: 
+
+        B,N,C,W,H = video.size()
+        video = video.view(-1,C,W,H)
+        video = video.to(device,non_blocking=True) 
+        video_feat = model.visual_encoder(video)        
+        video_embed = model.vision_proj(video_feat[:,0,:])   
+        video_embed = video_embed.view(B,N,-1).mean(dim=1)
+        video_embed = F.normalize(video_embed,dim=-1)  
+       
+        video_feat = video_feat.view(B,-1,video_feat.shape[-1])
+        video_feats.append(video_feat.cpu())
+        video_embeds.append(video_embed)
+     
+    video_feats = torch.cat(video_feats,dim=0)
+    video_embeds = torch.cat(video_embeds,dim=0)
+    
+    sims_matrix = video_embeds @ text_embeds.t()
+    score_matrix_v2t = torch.full((len(texts),len(texts)),-100.0).to(device) 
+    
+    num_tasks = utils.get_world_size()
+    rank = utils.get_rank() 
+    step = sims_matrix.size(0)//num_tasks + 1
+    start = rank*step
+    end = min(sims_matrix.size(0),start+step)
+
+    for i,sims in enumerate(metric_logger.log_every(sims_matrix[start:end], 50, header)): 
+        topk_sim, topk_idx = sims.topk(k=config['k_test'], dim=0)
+        
+        encoder_output = video_feats[start+i].repeat(config['k_test'],1,1).to(device,non_blocking=True) 
+        encoder_att = torch.ones(encoder_output.size()[:-1],dtype=torch.long).to(device,non_blocking=True) 
+        output = model.text_encoder(text_ids[topk_idx], 
+                                    attention_mask = text_atts[topk_idx],
+                                    encoder_hidden_states = encoder_output,
+                                    encoder_attention_mask = encoder_att,                             
+                                    return_dict = True,
+                                   )
+        score = model.itm_head(output.last_hidden_state[:,0,:])[:,1]
+        score_matrix_v2t[start+i,topk_idx] = score + topk_sim
+        
+    sims_matrix = sims_matrix.t()
+    score_matrix_t2v = torch.full((len(texts),len(texts)),-100.0).to(device) 
+    
+    step = sims_matrix.size(0)//num_tasks + 1
+    start = rank*step
+    end = min(sims_matrix.size(0),start+step)    
+    
+    for i,sims in enumerate(metric_logger.log_every(sims_matrix[start:end], 50, header)): 
+        
+        topk_sim, topk_idx = sims.topk(k=config['k_test'], dim=0)
+        encoder_output = video_feats[topk_idx].to(device,non_blocking=True) 
+        encoder_att = torch.ones(encoder_output.size()[:-1],dtype=torch.long).to(device,non_blocking=True) 
+        output = model.text_encoder(text_ids[start+i].repeat(config['k_test'],1), 
+                                    attention_mask = text_atts[start+i].repeat(config['k_test'],1),
+                                    encoder_hidden_states = encoder_output,
+                                    encoder_attention_mask = encoder_att,                             
+                                    return_dict = True,
+                                   )
+        score = model.itm_head(output.last_hidden_state[:,0,:])[:,1]
+        score_matrix_t2v[start+i,topk_idx] = score + topk_sim
+
+    if args.distributed:
+        dist.barrier()   
+        torch.distributed.all_reduce(score_matrix_v2t, op=torch.distributed.ReduceOp.SUM) 
+        torch.distributed.all_reduce(score_matrix_t2v, op=torch.distributed.ReduceOp.SUM)        
+        
+    total_time = time.time() - start_time
+    total_time_str = str(datetime.timedelta(seconds=int(total_time)))
+    print('Evaluation time {}'.format(total_time_str)) 
+
+    return score_matrix_v2t.cpu().numpy(), score_matrix_t2v.cpu().numpy()
+
+
+            
+@torch.no_grad()
+def itm_eval(scores_v2t, scores_t2v, txt2vmg, vid2txt):
+    
+    #Video->Text 
+    ranks = np.zeros(scores_v2t.shape[0])
+    for index,score in enumerate(scores_v2t):
+        inds = np.argsort(score)[::-1]
+        ranks[index] = np.where(inds == vid2txt[index])[0][0]
+
+    # Compute metrics
+    tr1 = 100.0 * len(np.where(ranks < 1)[0]) / len(ranks)
+    tr5 = 100.0 * len(np.where(ranks < 5)[0]) / len(ranks)
+    tr10 = 100.0 * len(np.where(ranks < 10)[0]) / len(ranks)
+  
+    #Text->Video 
+    ranks = np.zeros(scores_t2v.shape[0])
+    
+    for index,score in enumerate(scores_t2v):
+        inds = np.argsort(score)[::-1]
+        ranks[index] = np.where(inds == txt2vmg[index])[0][0]
+    
+    mdR = np.median(ranks+1)
+        
+    # Compute metrics
+    vr1 = 100.0 * len(np.where(ranks < 1)[0]) / len(ranks)
+    vr5 = 100.0 * len(np.where(ranks < 5)[0]) / len(ranks)
+    vr10 = 100.0 * len(np.where(ranks < 10)[0]) / len(ranks)        
+
+    tr_mean = (tr1 + tr5 + tr10) / 3
+    vr_mean = (vr1 + vr5 + vr10) / 3
+    r_mean = (tr_mean + vr_mean) / 2
+
+    eval_result =  {'txt_r1': tr1,
+                    'txt_r5': tr5,
+                    'txt_r10': tr10,
+                    'txt_r_mean': tr_mean,
+                    'vid_r1': vr1,
+                    'vid_r5': vr5,
+                    'vid_r10': vr10,
+                    'vid_r_mean': vr_mean,
+                    'vid_mdR': mdR,
+                    'r_mean': r_mean}
+    return eval_result
+
+
+
+
+def main(args, config):
+    utils.init_distributed_mode(args)    
+    
+    device = torch.device(args.device)
+
+    # fix the seed for reproducibility
+    seed = args.seed + utils.get_rank()
+    torch.manual_seed(seed)
+    np.random.seed(seed)
+    random.seed(seed)
+    cudnn.benchmark = True
+
+    #### Dataset #### 
+    print("Creating retrieval dataset")
+    test_dataset = VideoDataset(config['video_root'],config['ann_root'],num_frm=config['num_frm_test'],
+                                max_img_size=config['image_size'], frm_sampling_strategy='uniform')
+
+    test_loader = DataLoader(
+        test_dataset,
+        batch_size=config['batch_size'],
+        num_workers=4,
+        pin_memory=True,
+        drop_last=False,
+        shuffle=False,
+    )  
+
+    #### Model #### 
+    print("Creating model")
+    model = blip_retrieval(pretrained=config['pretrained'], image_size=config['image_size'], vit=config['vit'])
+    
+    model = model.to(device)   
+    
+    model_without_ddp = model
+    if args.distributed:
+        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
+        model_without_ddp = model.module   
+    
+    score_v2t, score_t2v, = evaluation(model_without_ddp, test_loader, model_without_ddp.tokenizer, device, config)
+
+    if utils.is_main_process():  
+
+        test_result = itm_eval(score_v2t, score_t2v, test_loader.dataset.txt2video, test_loader.dataset.video2txt)  
+        print(test_result)
+
+        log_stats = {**{f'{k}': v for k, v in test_result.items()},}
+        with open(os.path.join(args.output_dir, "test_result.txt"),"a") as f:
+            f.write(json.dumps(log_stats) + "\n")     
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()     
+    parser.add_argument('--config', default='./configs/retrieval_msrvtt.yaml')
+    parser.add_argument('--output_dir', default='output/Retrieval_msrvtt')        
+    parser.add_argument('--device', default='cuda')
+    parser.add_argument('--seed', default=42, type=int)
+    parser.add_argument('--world_size', default=1, type=int, help='number of distributed processes')    
+    parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training')
+    parser.add_argument('--distributed', default=True, type=bool)
+    args = parser.parse_args()
+
+    config = yaml.load(open(args.config, 'r'), Loader=yaml.Loader)
+
+    Path(args.output_dir).mkdir(parents=True, exist_ok=True)
+        
+    yaml.dump(config, open(os.path.join(args.output_dir, 'config.yaml'), 'w'))    
+    
+    main(args, config)
\ No newline at end of file