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ImageNet-1K Preparation

We use standard ImageNet dataset, you can download it from http://image-net.org/. We provide the following two ways to load data:

  • For standard folder dataset, move validation images to labeled sub-folders. The file structure should look like:

    $ tree data
    imagenet
    ├── train
    │   ├── class1
    │   │   ├── img1.jpeg
    │   │   ├── img2.jpeg
    │   │   └── ...
    │   ├── class2
    │   │   ├── img3.jpeg
    │   │   └── ...
    │   └── ...
    └── val
        ├── class1
        │   ├── img4.jpeg
        │   ├── img5.jpeg
        │   └── ...
        ├── class2
        │   ├── img6.jpeg
        │   └── ...
        └── ...
    
  • To boost the slow speed when reading images from massive small files, we also support zipped ImageNet, which includes four files:

    • train.zip, val.zip: which store the zipped folder for train and validate splits.
    • train_map.txt, val_map.txt: which store the relative path in the corresponding zip file and ground truth label. Make sure the data folder looks like this:
    $ tree data
    data
    └── ImageNet-Zip
        ├── train_map.txt
        ├── train.zip
        ├── val_map.txt
        └── val.zip
    
    $ head -n 5 data/ImageNet-Zip/val_map.txt
    ILSVRC2012_val_00000001.JPEG	65
    ILSVRC2012_val_00000002.JPEG	970
    ILSVRC2012_val_00000003.JPEG	230
    ILSVRC2012_val_00000004.JPEG	809
    ILSVRC2012_val_00000005.JPEG	516
    
    $ head -n 5 data/ImageNet-Zip/train_map.txt
    n01440764/n01440764_10026.JPEG	0
    n01440764/n01440764_10027.JPEG	0
    n01440764/n01440764_10029.JPEG	0
    n01440764/n01440764_10040.JPEG	0
    n01440764/n01440764_10042.JPEG	0