Table Of Contents
Table Of Contents

Prepare the ImageNet dataset

The ImageNet project contains millions of images and thounds of objects for image classification. It is widely used in the research community for benchmarking state-of-the-art models.

https://www.fanyeong.com/wp-content/uploads/2018/01/v2-718f95df083b2d715ee29b018d9eb5c2_r.jpg

The dataset has multiple versions. The one commonly used for image classification is ILSVRC 2012. This tutorial will go through the steps of preparing this dataset for GluonCV.

Note

You need at least 300 GB disk space to download and extract the dataset. SSD (Solid-state disks) is prefered over HDD because of faster speed.

Download

First, go to the download page (you may need to register an account), and find the page for ILSVRC2012. Next, find and download the following two files:

Filename Size
ILSVRC2012_img_train.tar 138 GB
ILSVRC2012_img_val.tar 6.3 GB

Setup

First, please download the helper script imagenet.py validation image info imagenet_val_maps.pklz. Make sure to put them in the same directory.

Assuming the tar files are saved in folder ~/ILSVRC2012. We can use the following command to prepare the dataset automatically.

python imagenet.py --download-dir ~/ILSVRC2012

Note

Extracting the images may take a while. For example, it takes about 30min on an AWS EC2 instance with EBS.

By default imagenet.py will extract the images into ~/.mxnet/datasets/imagenet. You can specify a different target folder by setting --target-dir.

Read with GluonCV

The prepared dataset can be loaded with utility class gluoncv.data.ImageNet directly. Here is an example that randomly reads 128 images each time and performs randomized resizing and cropping.

from gluoncv.data import ImageNet
from mxnet.gluon.data import DataLoader
from mxnet.gluon.data.vision import transforms

train_trans = transforms.Compose([
    transforms.RandomResizedCrop(224),
    transforms.ToTensor()
])

# You need to specify ``root`` for ImageNet if you extracted the images into
# a different folder
train_data = DataLoader(
    ImageNet(train=True).transform_first(train_trans),
    batch_size=128, shuffle=True)
for x, y in train_data:
    print(x.shape, y.shape)
    break

Out:

(128, 3, 224, 224) (128,)

Plot some validation images

from gluoncv.utils import viz
val_dataset = ImageNet(train=False)
viz.plot_image(val_dataset[1234][0])  # index 0 is image, 1 is label
viz.plot_image(val_dataset[4567][0])
  • ../../_images/sphx_glr_imagenet_001.png
  • ../../_images/sphx_glr_imagenet_002.png

Total running time of the script: ( 0 minutes 6.062 seconds)

Gallery generated by Sphinx-Gallery