Chandan Singh | Quick start imagenet in pytorch

quick start imagenet in pytorch

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ImageNet has become a staple dataset in computer vision, but is still pretty difficult to download/install. These are some simple instructions to get up and running in pytorch.

step 1: download/preprocessing

  • begin by following the instructions for downloading the ImageNet dataset here
  • the dataset contains ~1.2 million training images and 50,000 validation images
  • note that the dataset is quite large: the .tar files are 138G for train and 6.3G for val
    • once extracted the train data is 177G and the val data is 8.3G
  • the folder hierarchy looks liks this (val looks similar, there are 1000 folders, one for each class):
    train/
      n04550184/
          n04550184_9946.JPEG
          n04550184_9945.JPEG  
          ...
      n04550180/
      ...
      n04550180/
    

step 2: get the names for each class

  • to get the names for each of the classes, look at the class_names_imagenet.py file, which contains a dictionary containing the class labels

step 3: set up a dataloader

  • in pytorch, the dataloader should be set up using an ImageFolder
  • there is an example here