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^{Image Classification}

# 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.

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 an example of using the [`imageNet`](../c/imageNet.h) class, we provide sample programs for C++ and Python: - [`imagenet.cpp`](../examples/imagenet/imagenet.cpp) (C++) - [`imagenet.py`](../python/examples/imagenet.py) (Python) These samples are able to classify images, videos, and camera feeds. For more info about the various types of input/output streams supported, see the [Camera Streaming and Multimedia](aux-streaming.md) page. ### Using the ImageNet Program on Jetson First, let's try using the `imagenet` program to test imageNet recognition on some example images. It loads an image (or images), uses TensorRT and the `imageNet` class to perform the inference, then overlays the classification result and saves the output image. The project comes with sample images for you to use located under the `images/` directory. After [building](building-repo-2.md) the project, make sure your terminal is located in the `aarch64/bin` directory: ``` bash $ cd jetson-inference/build/aarch64/bin ``` Next, let's classify an example image with the `imagenet` program, using either the [C++](../examples/imagenet/imagenet.cpp) or [Python](../python/examples/imagenet.py) variants. If you're using the [Docker container](aux-docker.md), it's recommended to save the classified output image to the `images/test` mounted directory. These images will then be easily viewable from your host device in the `jetson-inference/data/images/test` directory (for more info, see [Mounted Data Volumes](aux-docker.md#mounted-data-volumes)). ``` bash # C++ $ ./imagenet images/orange_0.jpg images/test/output_0.jpg # (default network is googlenet) # Python $ ./imagenet.py images/orange_0.jpg images/test/output_0.jpg # (default network is googlenet) ``` > **note**: the first time you run each model, TensorRT will take a few minutes to optimize the network.
> this optimized network file is then cached to disk, so future runs using the model will load faster.

``` bash # C++ $ ./imagenet images/strawberry_0.jpg images/test/output_1.jpg # Python $ ./imagenet.py images/strawberry_0.jpg images/test/output_1.jpg ```

In addition to loading single images, you can also load a directory or sequence of images, or a video file. For more info, see the [Camera Streaming and Multimedia](aux-streaming.md) page or launch the application with the `--help` flag. ### Downloading Other Classification Models The following pre-trained image classification models are available to use and will be automatically downloaded (the default is `googlenet`): | Network | CLI argument | NetworkType enum | | --------------|----------------|------------------| | AlexNet | `alexnet` | `ALEXNET` | | GoogleNet | `googlenet` | `GOOGLENET` | | GoogleNet-12 | `googlenet-12` | `GOOGLENET_12` | | ResNet-18 | `resnet-18` | `RESNET_18` | | ResNet-50 | `resnet-50` | `RESNET_50` | | ResNet-101 | `resnet-101` | `RESNET_101` | | ResNet-152 | `resnet-152` | `RESNET_152` | | VGG-16 | `vgg-16` | `VGG-16` | | VGG-19 | `vgg-19` | `VGG-19` | | Inception-v4 | `inception-v4` | `INCEPTION_V4` | Generally the more complex networks can have greater classification accuracy, with increased runtime. ### Using Different Classification Models You can specify which model to load by setting the `--network` flag on the command line to one of the corresponding CLI arguments from the table above. By default, GoogleNet is loaded if the optional `--network` flag isn't specified. Below are some examples of using the ResNet-18 model: ``` bash # C++ $ ./imagenet --network=resnet-18 images/jellyfish.jpg images/test/output_jellyfish.jpg # Python $ ./imagenet.py --network=resnet-18 images/jellyfish.jpg images/test/output_jellyfish.jpg ```

``` bash # C++ $ ./imagenet --network=resnet-18 images/stingray.jpg images/test/output_stingray.jpg # Python $ ./imagenet.py --network=resnet-18 images/stingray.jpg images/test/output_stingray.jpg ```

``` bash # C++ $ ./imagenet --network=resnet-18 images/coral.jpg images/test/output_coral.jpg # Python $ ./imagenet.py --network=resnet-18 images/coral.jpg images/test/output_coral.jpg ```

Feel free to experiment with using the different models and see how their accuracies and performance differ - you can download more models with the [Model Downloader](building-repo-2.md#downloading-models) tool. There are also various test images found under `images/` ### Processing a Video The [Camera Streaming and Multimedia](aux-streaming.md) page shows the different types of streams that the `imagenet` program can handle. Here is an example of running it on a video from disk: ``` bash # Download test video (thanks to jell.yfish.us) $ wget https://nvidia.box.com/shared/static/tlswont1jnyu3ix2tbf7utaekpzcx4rc.mkv -O jellyfish.mkv # C++ $ ./imagenet --network=resnet-18 jellyfish.mkv images/test/jellyfish_resnet18.mkv # Python $ ./imagenet.py --network=resnet-18 jellyfish.mkv images/test/jellyfish_resnet18.mkv ```

Next we'll go through the steps to code your own image recognition program from scratch, first in Python and then C++. ##

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