6.12.3. Inference on YOLOv3 and Calculating Accuracy Scores
cd $COREDLA_WORK/runtime ./build_Release/dla_benchmark/dla_benchmark \ -b=1 \ -niter=5000 \ -m $COREDLA_WORK/demo/models/public/yolo-v3-tf/FP32/yolo-v3-tf.xml \ -d=HETERO:FPGA,CPU \ -i=./coco-images/val2017 \ -plugins_xml_file=./plugins.xml \ -arch_file=$COREDLA_ROOT/example_architectures/A10_Performance.arch \ -yolo_version=yolo-v3-tf \ -api=async \ -groundtruth_loc=./coco-images/groundtruth \ -nireq=4 \ -enable_object_detection_ap \ -perf_est \ -bgr
The dla_benchmark command prints the mAP and COCO AP scores and saves a text file called ap_report.txt that contains the scores in the current working directory.
To enable the accuracy checking routine for object detection graphs such as YOLOv3, use the -enable_object_detection_ap=1 option of the dla_benchmark command. This flag directs the dla_benchmark command to calculate the mAP and COCO AP for object detection graphs.
Also, specify the version of the YOLO graph that you provide to the dla_benchmark command with the -yolo_version= yolo-v3-tf option.
The input images folder is specified with -i=./coco-images and the ground truth annotations is specified with -groundtruth_loc=./groundtruth. If you chose to save the images or the ground truth annotations to a location other than the ones specified in this tutorial, update these parameters to point to the correct location.