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# Classifying Images with ImageNet
There are multiple types of deep learning networks available, including recognition, detection/localization, and semantic segmentation. The first deep learning capability we're highlighting in this tutorial is **image recognition**, using classifcation networks that have been trained on large datasets to identify scenes and objects.
<img src="https://github.com/dusty-nv/jetson-inference/raw/pytorch/docs/images/imagenet.jpg" width="900">
The [`imageNet`](../c/imageNet.h) object accepts an input image and outputs the probability for each class. Having been trained on the ImageNet ILSVRC dataset of **[1000 objects](../data/networks/ilsvrc12_synset_words.txt)**, the GoogleNet and ResNet-18 models were automatically downloaded during the build step. See [below](#downloading-other-classification-models) for other classification models that can be downloaded and used as well.
As examples of using [`imageNet`](../c/imageNet.h) we provide versions of a command-line interface for C++ and Python:
- [`imagenet-console.cpp`](../examples/imagenet-console/imagenet-console.cpp) (C++)
- [`imagenet-console.py`](../python/examples/imagenet-console.py) (Python)
Later in the tutorial, we'll also cover versions of a live camera recognition program for C++ and Python:
- [`imagenet-camera.cpp`](../examples/imagenet-camera/imagenet-camera.cpp) (C++)