QMNIST in PyTorch

explains can use QMNIST 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 what(Optional-Default:None-Type:str). *"train"(60,000 images), "test"(60,000 images), "test10k"(10,000 images), "test50k"(50,000 images) or "nist"(402,953 images) can be set to it.


• The 3rd argument is compat(Optional-Default:True-Type:bool). *If it's True, the class number of each image is returnd(for compatibility with the MNIST dataloader) while if it's False, the 1D tensor of the full qmnist information is returned.


• The 4th argument is train argument(Optional-Default:True-Type:bool):
  *Memos:
  
  
  
  
  • It's ignored if what isn't None.
  
  
  • If it's True, train data(60,000 images) is used while if it's False, test data(60,000 images) is used.
  
  
  
  
  
  



• There is transform argument(Optional-Default:None-Type:callable). *transform= must be used.



• There is target_transform argument(Optional-Default:None-Type:callable). *target_transform= must be used.



• There is download argument(Optional-Default:False-Type:bool):
  *Memos:
  
  
  
  
  • 
    download= must be used.
  
  
  • 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 from
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