OxfordIIITPet in PyTorch

explains can use Oxford-IIIT Pet dataset as shown below:

*Memos:

• The 1st argument is root(Required-Type:str or pathlib.Path). *An absolute or relative path is possible.


• The 2nd argument is split(Optional-Default:"train"-Type:str). *"trainval"(3,680 images) or "test"(3,669 images) can be set to it.


• The 3rd argument is target_types(Optional-Default:"attr"-Type:str or list of str):
  *Memos:
  
  
  
  
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    "category", "binary-category" and/or "segmentation" can be set to it:
    *Memos:
  
  
  • 
    "category" is for the label from 37 categories(classes).
  
  
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    "binary-category" is for the label of cat(0) or dog(1).
  
  
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    "segmentation" is for a segmentation trimap image.
  
  
  • An empty tuple or list can also be set to it.
  
  
  • The multiple same values can be set to it.
  
  
  • If the order of values is different, the order of their elements is also different.
  
  
  
  
  
  



• The 4th argument is transform(Optional-Default:None-Type:callable).



• The 5th argument is target_transform(Optional-Default:None-Type:callable).



• The 6th argument is download(Optional-Default:False-Type:bool):
  *Memos:
  
  
  
  
  • If it's True, the dataset is downloaded from the internet and extracted(unzipped) to root.
  
  
  • If it's True and the dataset is already downloaded, it's extracted.
  
  
  • If it's True and the dataset is already downloaded and extracted, nothing happens.
  
  
  • It should be False if the dataset is already downloaded and extracted because it's faster.
  
  
  • You can manually download and extract the dataset(images.tar.gz and annotations.tar.gz) from
    
    
    
    
    
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