Legacy DNNDK Examples - 1.1 English

ResNet-50

$dnndk_sample_base/resnet50 contains an example of image classification using Caffe ResNet-50 model. It reads the images under the $dnndk_sample_base/dataset/image500_640_480 directory and outputs the classification result for each input image. You can then launch it with the./resnet50 command.

Video Analytics

An object detection example is located under the $dnndk_sample_base/video_analysis directory. It reads image frames from a video file and annotates detected vehicles and pedestrians in real-time. Launch it with the command ./video_analysis video/structure.mp4 (where video/structure.mp4 is the input video file).

ADAS Detection

An example of object detection for ADAS (Advanced Driver Assistance Systems) application using YOLO-v3 network model is located under the directory $dnndk_sample_base/adas_detection. It reads image frames from a video file and annotates in real-time. Launch it with the ./adas_detection video/adas.avi command (where video/adas.mp4 is the input video file).

Semantic Segmentation

An example of semantic segmentation in the $dnndk_sample_base/segmentation directory. It reads image frames from a video file and annotates in real-time. Launch it with the ./segmentation video/traffic.mp4 command (where video/traffic.mp4 is the input video file).

Inception-v1 with Python

$dnndk_sample_base/inception_v1_mt_py contains a multithreaded image classification example of Inception-v1 network developed with the advanced Python APIs. With the command python3 inception_v1_mt.py 4, it will run with four threads. The throughput (in fps) will be reported after it completes.

Different from C++ examples, Incpetion-v1 model is compiled to DPU ELF file first and then it is transformed into the DPU shared library libdpumodelinception_v1.so with the following command on the evaluation board. dpu_inception_v1_*.elf means to include all DPU ELF files in case that Incpetion-v1 is compiled into several DPU kernels by VAI_C compiler. Refer to the section of DPU Shared Library for more details.

 aarch64-xilinx-linux-gcc -fPIC -shared \	
 dpu_inception_v1_*.elf -o libdpumodelinception_v1.so

Within the Vitis AI cross compilation environment on host, use the following command for this purpose instead.

source /opt/petalinux/2019.2/environment-setup-aarch64-xilinx-linux
aarch64-xilinx-linux-gcc \
--sysroot=$SDKTARGETSYSROOT \
-fPIC -shared dpu_inception_v1_*.elf -o libdpumodelinception_v1.so

minResNet with Python

$dnndk_sample_base/mini_resnet_py contains the image classification example of TensorFlow minResNet network developed with Vitis AI advanced Python APIs. With the command python3 mini_resnet.py, the results of top-5 labels and corresponding probabilities are displayed. miniResNet is described in the second book Practitioner Bundle of the Deep Learning for CV with Python series. It is a customization of the original ResNet-50 model and is also well explained in the third book ImageNet Bundle from the same book’s series.

YOLO-v3 with Python

$dnndk_sample_base/tf_yolov3_voc_py contains the object detection example of TensorFlow YOLOv3 network developed with Vitis AI advanced Python APIs. With the command python3 tf_yolov3_voc.py, the result image after object detection is displayed.