imagenet-console-2.md

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Image Recognition}

Classifying Images with ImageNet

There are multiple types of deep learning networks available, including recognition, detection/localization, and soon segmentation. The first deep learning capability we're highlighting in this tutorial is image recognition using an 'imageNet' that's been trained to identify similar objects.

The imageNet object accepts an input image and outputs the probability for each class. Having been trained on ImageNet database of 1000 objects, the standard AlexNet and GoogleNet networks were automatically downloaded during the build step. As examples of using imageNet we provide a command-line interface called imagenet-console and a live camera program called imagenet-camera.

Using the Console Program on Jetson

First, try using the imagenet-console program to test imageNet recognition on some example images. It loads an image, uses TensorRT and the imageNet class to perform the inference, then overlays the classification and saves the output image.

After building, make sure your terminal is located in the aarch64/bin directory:

$ cd jetson-inference/build/aarch64/bin

Then, classify an example image with the imagenet-console program. imagenet-console accepts 2 command-line arguments: the path to the input image and path to the output image (with the class overlay printed).

$ ./imagenet-console orange_0.jpg output_0.jpg

note: the first time you run the program, TensorRT may take up to a few minutes to optimize the network.
          this optimized network file is cached to disk after the first run, so future runs will load faster.

$ ./imagenet-console granny_smith_1.jpg output_1.jpg

Next, we'll go through the steps to code your own image recognition program from scratch.

Next | Coding Your Own Image Recognition Program
Back | Building the Repo from Source

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