ImageNet and its Applications


Basically, ImageNet is a visual image dataset that contains a large number of images. In fact, the dataset contains more than 14 million images of over 22000 categories. Specifically, this dataset is created for solving computer vision problems. It is used for object recognition and the images that it contains are manually labeled.

Applications of ImageNet

In the first place, ImageNet is used for image classification. Hence, the most important application of ImageNet is object recognition. Also, the dataset has many applications in Machine Learning. Since, for creating a machine learning model, it requires adequate training. Therefore, we can use the ImageNet dataset for training the model. Further, this trained model can be used to identify objects.

Moreover, we can use pre-trained ImageNet models in Transfer Learning applications. For instance, we can perform image recognition tasks using VGG or ResNet. Evidently, these are the pre-trained Convolution Neural Network models for deep learning.

Accessing ImageNet

For the purpose of accessing ImageNet, you can go to the Kaggle ImageNet Localization Challenge where you can download the dataset. However, it is a very large dataset of size more than 150GB.

Further, training a Machine Learning model from scratch using ImageNet is a very time-consuming task. In fact, training a CNN model with this dataset can even take days. However, you can use any of the pre-trained models that can help you in specific Computer Vision applications. The following list provides some of the pre-trained ImageNet models.

  • In short, VGG16 is a CNN architecture. Basically, VGG stands for Visual Geometry Group and is used in many Computer Vision applications such as Face Recognition.
  • Apart from VGG16, there is also a VGG19 model that has a 19-layer deep CNN. Like VGG16 it is also traing on the ImageNet dataset and used in Image Classification.
  • Another pre-trained model is ResNet50 that has 48 convolution layers. Specifically, it is also used for image classification.
  • Likewise, Inception v3 is also an image recognition and classification model that is trained on the ImageNet dataset.
  • Similarly, Xception is another trained CNN model. Further, the model has 71 layers and can achieve a classification accuracy of up to 90%.


In short, the ImageNet dataset makes Transfer Learning possible. Earlier, the image classification task relied on the training of a model for a specific category. In other words, for vehicle classification, we required images of vehicles. Moreover, the Convolution Neural Networks also trained on specific categories of the image datasets.

Therefore, with the use of ImageNet for training a model, we need to perform the training once. Subsequently, we can use the trained model to classify the images of any category. Being that, it is used in a wide range of applications. Particularly, the models trained on this dataset are used in medical diagnostics. Another application of models trained on this dataset is autonomous vehicles.

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