Hi,
This is the value for normalizing the kinetics dataset for video classification in the official script.

  
      github.com
  
  
    pytorch/vision/blob/master/references/video_classification/train.py#L115 2

print("torchvision version: ", torchvision.__version__)device = torch.device(args.device)torch.backends.cudnn.benchmark = True# Data loading codeprint("Loading data")traindir = os.path.join(args.data_path, args.train_dir)valdir = os.path.join(args.data_path, args.val_dir)normalize = T.Normalize(mean=[0.43216, 0.394666, 0.37645],                        std=[0.22803, 0.22145, 0.216989])print("Loading training data")st = time.time()cache_path = _get_cache_path(traindir)transform_train = torchvision.transforms.Compose([    ConvertBHWCtoBCHW(),    T.ConvertImageDtype(torch.float32),    T.Resize((128, 171)),    T.RandomHorizontalFlip(),



  
  
    
    
  
  


i.e.,
normalize = T.Normalize(mean=[0.43216, 0.394666, 0.37645],
std=[0.22803, 0.22145, 0.216989])
And in https://pytorch.org/docs/stable/torchvision/models.html 1
it says that “All pre-trained models expect input images normalized in the same way”
with this norm transform:
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
My case is to use pretrained resnet in torchvision to extract feature on kinetics video frames.
So which norm should I choose?
And how these values come from?
Thanks.