This repository contains the code for Darknet, an open-source neural network framework. Follow the steps below to clone the repository, build Darknet, download the weights, and run the object detector.
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Clone the darknet repository
-
Change your current directory to the cloned repository:
cd darknet
- Build Darknet by running the make command:
make
- Download the weights for the YOLOv3 model using wget:
wget https://pjreddie.com/media/files/yolov3.weights
- Run the object detector and benchmark by executing the following command:
./darknet detect cfg/yolov3.cfg yolov3.weights data/dog.jpg
- You will see some output like this:
layer filters size input output
0 conv 32 3 x 3 / 1 416 x 416 x 3 -> 416 x 416 x 32 0.299 BFLOPs
1 conv 64 3 x 3 / 2 416 x 416 x 32 -> 208 x 208 x 64 1.595 BFLOPs
.......
105 conv 255 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 255 0.353 BFLOPs
106 detection
truth_thresh: Using default '1.000000'
Loading weights from yolov3.weights...Done!
data/dog.jpg: Predicted in 0.029329 seconds.
dog: 99%
truck: 93%
bicycle: 99%
On your laptop, navigate to the Darknet source code directory. Open the Makefile
and locate the following lines:
CC=gcc
CPP=g++
Replace them with
CC=aarch64-linux-gnu-gcc
CPP=aarch64-linux-gnu-gcc
These changes configure Darknet to use the aarch64-linux-gnu-gcc cross-compiler for compilation.
With the Makefile configured for cross-compilation, you can now compile Darknet on BeagleBoard AI64:
CC=arm-linux-gnueabi-gcc make -j
To transfer the compiled Darknet files from your laptop to BeagleBoard AI64, use the SCP (Secure Copy Protocol). Run the following command on your laptop:
scp /path/to/compiled/darknet beagle@beagleboard-ai64:/destination/on/beagleboard
In my case, the command is
scp -r ./darknet [email protected]:/home/debian
You can now execute Darknet on your BeagleBoard AI64:
./darknet detect cfg/yolov3.cfg yolov3.weights data/dog.jpg