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{
"_id": {
"$oid": "652bbe8d115ff300cbd113e6"
},
"network_id": "nanodet_plus_m_x1.5_416_imx500v1_mctq_Keras",
"date_inserted": {
"$date": {
"$numberLong": "1697371203072"
}
},
"network_file": "/data/projects/swat/network_database/Tensorflow2/internal/nanodet-plus-m/nanodet-plus-m-x1.5-416_quant.keras",
"convpy_cli_supp": "",
"name": "nanodet_plus_m_x1.5_mctq",
"framework": "Tensorflow2",
"accuracy_measurements": [
"Top1",
{
"top1": 0.3316
}
],
"background": false,
"dataset_id": "CocoPostprocessNANODET",
"preprocess": [
{
"ResizeBilinear": {
"name": "Resize",
"new_height": 416,
"new_width": 416
}
},
{
"RGBtoBGR": {
"name": "RGBtoBGR"
}
},
{
"Normalize": {
"name": "Normalization",
"mean": [
103.53,
116.28,
123.675
],
"std": [
57.375,
57.12,
58.395
]
}
}
],
"task_type": "Object_Detection",
"inputs_type": [
"RGB"
],
"outputs_type": [
"boxes",
"classes",
"scores"
],
"sdsp_conv_cli_supp": "--no-input-persistency",
"quantization_type": "MCTQ"
}