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rtmdet_nonms.yaml
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rtmdet_nonms.yaml
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%YAML:1.0
---
#model_path: "/home/zli/Documents/deeplearning/code_mmpose_snpe/models/rtmdet_tiny_nonms_640_210.dlc"
## 注rtmdet_nano_320_210.dlc只有一个类别,使用时需要修改 RtmDET::score_node_nums = 1;
model_path: "/home/zli/Documents/deeplearning/code_mmpose_snpe/models/rtmdet_nano_nonms_320_210.dlc"
model_input_layer_name: ["input"]
#model_output_layer_name: ["Concat_396", "Concat_394"]
#model_output_tensor_name: ["1036", "1034"]
model_output_layer_name: ["Concat_502", "Concat_500"]
model_output_tensor_name: ["onnx::Gather_1404", "onnx::Sigmoid_1402"]
#model_input_width: 640
#model_input_hight: 640
model_input_width: 320
model_input_hight: 320
#moudel_input_shape: [1, 640, 640, 3]
#moudel_output_shape: [ [1, 8400, 4], [1, 8400, 80]]
moudel_input_shape: [1, 320, 320, 3]
moudel_output_shape: [ [1, 2100, 4], [1, 2100, 80]]
# 0->snpe 1->onnx 2->ncnn 3->dnn
framework: 0
# 0->cpu 1->gpu 2->dsp 3->aip
runtime: 0
#nms parameter
target_conf_th: 0.5
nms_th: 0.2
model_with_nms: false
udo_list: ["/home/zli/Documents/deeplearning/code_mmpose_snpe/snpe_udo/libUdoHardSigmodUdoPackageReg.so"]