Testing DetectionSuite

Testing detectionsuite

As an example you can use Pascal VOC dataset on darknet format using the following instructions to convert to the desired format:

wget https://pjreddie.com/media/files/VOCtrainval_11-May-2012.tar
wget https://pjreddie.com/media/files/VOCtrainval_06-Nov-2007.tar
wget https://pjreddie.com/media/files/VOCtest_06-Nov-2007.tar
tar xf VOCtrainval_11-May-2012.tar
tar xf VOCtrainval_06-Nov-2007.tar
tar xf VOCtest_06-Nov-2007.tar

wget https://pjreddie.com/media/files/voc_label.py
python voc_label.py
cat 2007_train.txt 2007_val.txt 2012_*.txt > train.txt

In order to use darknet to detect objects over the images you have to download the network configuration and the network weights [5] and [6]. Then set the corresponding paths into DeepLearningSuite/appConfig.txt. You have also to create a file with the corresponding name for each class detection for darknet, you can download the file directly from [7]

Once you have your custom appConfig.txt you can run the DatasetEvaluationApp.

Using TensorFlow:

TensorFlow can also be used for Object Detection in this tool. All you need is a frozen inference graph and a video to run inferences on. Some sample pre-trained graphs are available at TensorFlow Object Detection Model Zoo trained on datasets such as COCO, KITTI and Open Images.

More instructions and tutorials on using the same are mentioned here(Github's Wiki).

References.

[1] http://tracking.cs.princeton.edu/dataset.html
[2] http://www2.informatik.uni-freiburg.de/~spinello/RGBD-dataset.html
[3] YOLO: https://pjreddie.com/darknet/yolo/
[4] YOLO with c++ API: https://github.com/jderobot/darknet
[5] https://pjreddie.com/media/files/yolo-voc.weights
[6] https://github.com/pjreddie/darknet/blob/master/cfg/yolo-voc.cfg
[7] https://github.com/pjreddie/darknet/blob/master/data/voc.names