Running AlexNet on Raspberry Pi with Arm Compute Library
Overview Prerequisites Introducing the Graph API Introducing AlexNet Evaluate the example code Download and install the tutorial ZIP file Compile the Arm Compute Library Run the classifier Develop your own network using the Arm Compute Library
Run the classifier
The CNN has been trained to recognise 1000 object categories. A go-kart is one of these. This step demonstrates how the object is recognized when the CNN is passed an image of a go-kart. In contrast, if you defined a random image which is not part of the 1000 categories then the CNN will not be able to recognize it it.
If you compiled natively on your Raspberry Pi, enter the following on the command line to run the classifier against the go_kart.ppm image:
export LD_LIBRARY_PATH=build/ PATH_ASSETS=../assets_alexnet ./build/examples/graph_alexnet 0 $PATH_ASSETS $PATH_ASSETS/go_kart.ppm $PATH_ASSETS/labels.txt
Or, if you cross-compiled, on your host machine open an SSH session by entering:
And in the SSH session, enter the Desktop folder and run the classifier against the go-kart .ppm image:
cd Desktop export LD_LIBRARY_PATH=build/ PATH_ASSETS=../assets_alexnet ./build/examples/graph_alexnet 0 $PATH_ASSETS $PATH_ASSETS/go_kart.ppm $PATH_ASSETS/labels.txt
Whether or not you are building the library natively, the output should look like this if a successful classification has been performed:
This screen-shot shows that the classifier has provided five predictions of the content of the image against the object categories that the CNN has been trained with. If the output does not look like the screen-shot, then it is highly likely that the assets have not been correctly copied to the SD card.
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