Intel® FPGA AI Suite: Getting Started Guide

ID 768970
Date 9/06/2023

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6.12.3. Inference on YOLOv3 and Calculating Accuracy Scores

To run inference on YOLOv3 and calculate the mAP and COCO AP scores, run the following commands:
cd $COREDLA_WORK/runtime
./build_Release/dla_benchmark/dla_benchmark \ 
   -b=1 \ 
   -niter=5000 \ 
   -m $COREDLA_WORK/demo/models/public/yolo-v3-tf/FP32/yolo-v3-tf.xml \ 
   -i=./coco-images/val2017 \ 
   -plugins_xml_file=./plugins.xml \ 
   -arch_file=$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 \ 

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.