Hi I’m trying to run the Facial Estimation Landmark model with 104 keypoints
• Hardware Platform: GPU
• DeepStream Version: docker image deepstream:6.0.1-devel
• NVIDIA GPU Driver Version: 470.103.01
• Cuda Version: 11.4
I pulled the docker image stated above and i followed the exact steps stated in this NVIDIA repo (deepstream_tao_apps/apps/tao_others/deepstream-faciallandmark-app at master · NVIDIA-AI-IOT/deepstream_tao_apps · GitHub)
I changed the number of landmarks to 104 instead of 80 in the following file:
However, when executing the binary it still displayed that the output is 80 as shown below:
INFO: ../nvdsinfer/nvdsinfer_model_builder.cpp:610 [FullDims Engine Info]: layers num: 4 0 INPUT kFLOAT input_face_images 1x80x80 min: 1x1x80x80 opt: 32x1x80x80 Max: 32x1x80x80 1 OUTPUT kFLOAT conv_keypoints_m80 80x80x80 min: 0 opt: 0 Max: 0 2 OUTPUT kFLOAT softargmax 80x2 min: 0 opt: 0 Max: 0 3 OUTPUT kFLOAT softargmax:1 80 min: 0 opt: 0 Max: 0
The Executed binary:
./deepstream-faciallandmark-app 1 /workspace/deepstream_tao_apps/configs/facial_tao/sample_faciallandmarks_config.txt file:///workspace/deepstream_tao_apps/apps/tao_others/deepstream-faciallandmark-app/face-2.jpg ./landmarks
I tried to print the coordinates of the landmarks points that had index above 80 they were 0 all the time. Moreover, the output image with the landmarks plotted on it always showed 80 points.
I would like to add that the model i’m using is the latest version of the deployable model downloaded from nvidia’s website.
How i can predict 104 facial landmark points ?