![]() This API takes care of any preprocessing the image needs (resizing, etc) cf_openpose_aichallenger_368_368_0.3_189.7G which can be found in Vitis AI Model Zooĭoing inference with OpenPose is quite simple: auto image = cv::imread("sample_openpose.jpg") auto det = vitis::ai::OpenPose::create("openpose_pruned_0_3") auto results = det->run(image). ![]() The TensorFlow2 and Vitis AI design flow is described in this tutorial. Python train-res.py python -u quantize.py -float_model model/residual/res.h5 -quant_model model/residual/quant_res.h5 -batchsize 64 -evaluate 2>&1 | tee quantize.log vai_c_tensorflow2 -model model/residual/quant_res.h5 -arch /opt/vitis_ai/compiler/arch/DPUCVDX8H/VCK5000/arch.json -output_dir model/residual -net_name res
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