<
   ir_version: 6,
   opset_import: ["" : 19],
   producer_name: "pytorch"
>
"torch-jit-export" (uint8[batch,height,width,3] image) => (float[batch,anchors8,1] scores8, float[batch,anchors16,1] scores16, float[batch,anchors32,1] scores32, float[batch,anchors8,4] boxes8, float[batch,anchors16,4] boxes16, float[batch,anchors32,4] boxes32, float[batch,anchors8,10] kps8, float[batch,anchors16,10] kps16, float[batch,anchors32,10] kps32) 
   <
      float[batch,12,unk__0,unk__1] "273"
      float[batch,12,unk__0,unk__1] "276"
      float[batch,24,unk__0,unk__1] "279"
      float[batch,24,unk__6,unk__7] "280"
      float[batch,24,unk__6,unk__7] "283"
      float[batch,24,unk__6,unk__7] "286"
      float[batch,24,unk__6,unk__7] "287"
      float[batch,24,unk__6,unk__7] "290"
      float[batch,24,unk__6,unk__7] "293"
      float[batch,24,unk__6,unk__7] "294"
      float[batch,24,unk__6,unk__7] "297"
      float[batch,24,unk__6,unk__7] "300"
      float[batch,24,unk__6,unk__7] "301"
      float[batch,48,unk__26,unk__27] "304"
      float[batch,24,unk__26,unk__27] "307"
      float[batch,48,unk__26,unk__27] "310"
      float[batch,48,unk__26,unk__27] "311"
      float[batch,48,unk__26,unk__27] "314"
      float[batch,48,unk__26,unk__27] "317"
      float[batch,48,unk__26,unk__27] "318"
      float[batch,48,unk__26,unk__27] "321"
      float[batch,48,unk__26,unk__27] "324"
      float[batch,48,unk__26,unk__27] "325"
      float[batch,48,unk__26,unk__27] "328"
      float[batch,48,unk__26,unk__27] "331"
      float[batch,48,unk__26,unk__27] "332"
      float[batch,48,unk__26,unk__27] "335"
      float[batch,48,unk__26,unk__27] "338"
      float[batch,48,unk__26,unk__27] "339"
      float[batch,48,unk__60,unk__61] "342"
      float[batch,48,unk__60,unk__61] "345"
      float[batch,48,unk__60,unk__61] "348"
      float[batch,48,unk__60,unk__61] "349"
      float[batch,48,unk__60,unk__61] "352"
      float[batch,48,unk__60,unk__61] "355"
      float[batch,48,unk__60,unk__61] "356"
      float[batch,48,unk__60,unk__61] "359"
      float[batch,48,unk__60,unk__61] "362"
      float[batch,48,unk__60,unk__61] "363"
      float[batch,80,unk__82,unk__83] "366"
      float[batch,48,unk__82,unk__83] "369"
      float[batch,80,unk__82,unk__83] "372"
      float[batch,80,unk__82,unk__83] "373"
      float[batch,80,unk__82,unk__83] "376"
      float[batch,80,unk__82,unk__83] "379"
      float[batch,80,unk__82,unk__83] "380"
      float[batch,24,unk__26,unk__27] "381"
      float[batch,24,unk__60,unk__61] "382"
      float[batch,24,unk__82,unk__83] "383"
      float[batch,24,unk__60,unk__61] "402"
      float[batch,24,unk__60,unk__61] "403"
      float[batch,24,unk__26,unk__27] "422"
      float[batch,24,unk__26,unk__27] "423"
      float[batch,24,unk__26,unk__27] "424"
      float[batch,24,unk__60,unk__61] "425"
      float[batch,24,unk__82,unk__83] "426"
      float[batch,24,unk__60,unk__61] "427"
      float[batch,24,unk__60,unk__61] "428"
      float[batch,24,unk__82,unk__83] "429"
      float[batch,24,unk__82,unk__83] "430"
      float[batch,24,unk__60,unk__61] "431"
      float[batch,24,unk__82,unk__83] "432"
      float[batch,64,unk__26,unk__27] "435"
      float[batch,64,unk__26,unk__27] "438"
      float[batch,64,unk__60,unk__61] "455"
      float[batch,64,unk__60,unk__61] "458"
      float[batch,64,unk__82,unk__83] "475"
      float[batch,64,unk__82,unk__83] "478"
      float[batch,12,unk__0,unk__1] "493"
      float[batch,12,unk__0,unk__1] "496"
      float[batch,24,unk__0,unk__1] "499"
      float[batch,24,unk__6,unk__7] "502"
      float[batch,24,unk__6,unk__7] "505"
      float[batch,24,unk__6,unk__7] "508"
      float[batch,24,unk__6,unk__7] "511"
      float[batch,24,unk__6,unk__7] "514"
      float[batch,24,unk__6,unk__7] "517"
      float[batch,48,unk__26,unk__27] "520"
      float[batch,48,unk__26,unk__27] "523"
      float[batch,48,unk__26,unk__27] "526"
      float[batch,48,unk__26,unk__27] "529"
      float[batch,48,unk__26,unk__27] "532"
      float[batch,48,unk__26,unk__27] "535"
      float[batch,48,unk__26,unk__27] "538"
      float[batch,48,unk__26,unk__27] "541"
      float[batch,48,unk__26,unk__27] "544"
      float[batch,48,unk__26,unk__27] "547"
      float[batch,48,unk__26,unk__27] "550"
      float[batch,48,unk__60,unk__61] "553"
      float[batch,48,unk__60,unk__61] "556"
      float[batch,48,unk__60,unk__61] "559"
      float[batch,48,unk__60,unk__61] "562"
      float[batch,48,unk__60,unk__61] "565"
      float[batch,48,unk__60,unk__61] "568"
      float[batch,48,unk__60,unk__61] "571"
      float[batch,80,unk__82,unk__83] "574"
      float[batch,80,unk__82,unk__83] "577"
      float[batch,80,unk__82,unk__83] "580"
      float[batch,80,unk__82,unk__83] "583"
      float[batch,80,unk__82,unk__83] "586"
      float[batch,64,unk__26,unk__27] "589"
      float[batch,64,unk__26,unk__27] "592"
      float[batch,64,unk__60,unk__61] "595"
      float[batch,64,unk__60,unk__61] "598"
      float[batch,64,unk__82,unk__83] "601"
      float[batch,64,unk__82,unk__83] "604"
      float[batch,3,height,width] "input.1"
      float[batch,height,width,3] tmp
      float[batch,3,height,width] tmp_0
      float[batch,30,unk__26,unk__27] val_0
      float[batch,unk__26,unk__27,30] val_1
      float[batch,anchors32,15] val_10
      float[batch,anchors32,1] val_11
      float[batch,anchors8,15] val_2
      float[batch,anchors8,1] val_3
      float[batch,30,unk__60,unk__61] val_4
      float[batch,unk__60,unk__61,30] val_5
      float[batch,anchors16,15] val_6
      float[batch,anchors16,1] val_7
      float[batch,30,unk__82,unk__83] val_8
      float[batch,unk__82,unk__83,30] val_9
   >
{
   [n0] tmp = Cast <to: int = 1> (image)
   [n1] tmp_0 = Transpose <perm: ints = [0, 3, 1, 2]> (tmp)
   [n4] "input.1" = Sub (tmp_0, const_cast)
   [Conv_0] "493" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [2, 2]> ("input.1", "494", "495")
   [Relu_1] "273" = Relu ("493")
   [Conv_2] "496" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> ("273", "497", "498")
   [Relu_3] "276" = Relu ("496")
   [Conv_4] "499" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> ("276", "500", "501")
   [Relu_5] "279" = Relu ("499")
   [MaxPool_6] "280" = MaxPool <ceil_mode: int = 0, kernel_shape: ints = [2, 2], pads: ints = [0, 0, 0, 0], strides: ints = [2, 2]> ("279")
   [Conv_7] "502" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> ("280", "503", "504")
   [Relu_8] "283" = Relu ("502")
   [Conv_9] "505" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> ("283", "506", "507")
   [Add_10] "286" = Add ("505", "280")
   [Relu_11] "287" = Relu ("286")
   [Conv_12] "508" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> ("287", "509", "510")
   [Relu_13] "290" = Relu ("508")
   [Conv_14] "511" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> ("290", "512", "513")
   [Add_15] "293" = Add ("511", "287")
   [Relu_16] "294" = Relu ("293")
   [Conv_17] "514" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> ("294", "515", "516")
   [Relu_18] "297" = Relu ("514")
   [Conv_19] "517" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> ("297", "518", "519")
   [Add_20] "300" = Add ("517", "294")
   [Relu_21] "301" = Relu ("300")
   [Conv_22] "520" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [2, 2]> ("301", "521", "522")
   [Relu_23] "304" = Relu ("520")
   [Conv_24] "523" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> ("304", "524", "525")
   [AveragePool_25] "307" = AveragePool <ceil_mode: int = 1, kernel_shape: ints = [2, 2], pads: ints = [0, 0, 0, 0], strides: ints = [2, 2]> ("301")
   [Conv_26] "526" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("307", "527", "528")
   [Add_27] "310" = Add ("523", "526")
   [Relu_28] "311" = Relu ("310")
   [Conv_29] "529" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> ("311", "530", "531")
   [Relu_30] "314" = Relu ("529")
   [Conv_31] "532" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> ("314", "533", "534")
   [Add_32] "317" = Add ("532", "311")
   [Relu_33] "318" = Relu ("317")
   [Conv_34] "535" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> ("318", "536", "537")
   [Relu_35] "321" = Relu ("535")
   [Conv_36] "538" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> ("321", "539", "540")
   [Add_37] "324" = Add ("538", "318")
   [Relu_38] "325" = Relu ("324")
   [Conv_39] "541" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> ("325", "542", "543")
   [Relu_40] "328" = Relu ("541")
   [Conv_41] "544" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> ("328", "545", "546")
   [Add_42] "331" = Add ("544", "325")
   [Relu_43] "332" = Relu ("331")
   [Conv_44] "547" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> ("332", "548", "549")
   [Relu_45] "335" = Relu ("547")
   [Conv_46] "550" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> ("335", "551", "552")
   [Add_47] "338" = Add ("550", "332")
   [Relu_48] "339" = Relu ("338")
   [Conv_49] "553" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [2, 2]> ("339", "554", "555")
   [Relu_50] "342" = Relu ("553")
   [Conv_51] "556" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> ("342", "557", "558")
   [AveragePool_52] "345" = AveragePool <ceil_mode: int = 1, kernel_shape: ints = [2, 2], pads: ints = [0, 0, 0, 0], strides: ints = [2, 2]> ("339")
   [Conv_53] "559" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("345", "560", "561")
   [Add_54] "348" = Add ("556", "559")
   [Relu_55] "349" = Relu ("348")
   [Conv_56] "562" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> ("349", "563", "564")
   [Relu_57] "352" = Relu ("562")
   [Conv_58] "565" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> ("352", "566", "567")
   [Add_59] "355" = Add ("565", "349")
   [Relu_60] "356" = Relu ("355")
   [Conv_61] "568" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> ("356", "569", "570")
   [Relu_62] "359" = Relu ("568")
   [Conv_63] "571" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> ("359", "572", "573")
   [Add_64] "362" = Add ("571", "356")
   [Relu_65] "363" = Relu ("362")
   [Conv_66] "574" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [2, 2]> ("363", "575", "576")
   [Relu_67] "366" = Relu ("574")
   [Conv_68] "577" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> ("366", "578", "579")
   [AveragePool_69] "369" = AveragePool <ceil_mode: int = 1, kernel_shape: ints = [2, 2], pads: ints = [0, 0, 0, 0], strides: ints = [2, 2]> ("363")
   [Conv_70] "580" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("369", "581", "582")
   [Add_71] "372" = Add ("577", "580")
   [Relu_72] "373" = Relu ("372")
   [Conv_73] "583" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> ("373", "584", "585")
   [Relu_74] "376" = Relu ("583")
   [Conv_75] "586" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> ("376", "587", "588")
   [Add_76] "379" = Add ("586", "373")
   [Relu_77] "380" = Relu ("379")
   [Conv_78] "381" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("339", "neck.lateral_convs.0.conv.weight", "neck.lateral_convs.0.conv.bias")
   [Conv_79] "382" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("363", "neck.lateral_convs.1.conv.weight", "neck.lateral_convs.1.conv.bias")
   [Conv_80] "383" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [1, 1], pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> ("380", "neck.lateral_convs.2.conv.weight", "neck.lateral_convs.2.conv.bias")
   [Resize_99] "402" = Resize <coordinate_transformation_mode: string = "asymmetric", cubic_coeff_a: float = -0.75, mode: string = "nearest", nearest_mode: string = "floor"> ("383", "", Resize_99_scales_2x)
   [Add_100] "403" = Add ("382", "402")
   [Resize_119] "422" = Resize <coordinate_transformation_mode: string = "asymmetric", cubic_coeff_a: float = -0.75, mode: string = "nearest", nearest_mode: string = "floor"> ("403", "", Resize_99_scales_2x)
   [Add_120] "423" = Add ("381", "422")
   [Conv_121] "424" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> ("423", "neck.fpn_convs.0.conv.weight", "neck.fpn_convs.0.conv.bias")
   [Conv_122] "425" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> ("403", "neck.fpn_convs.1.conv.weight", "neck.fpn_convs.1.conv.bias")
   [Conv_123] "426" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> ("383", "neck.fpn_convs.2.conv.weight", "neck.fpn_convs.2.conv.bias")
   [Conv_124] "427" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [2, 2]> ("424", "neck.downsample_convs.0.conv.weight", "neck.downsample_convs.0.conv.bias")
   [Add_125] "428" = Add ("425", "427")
   [Conv_126] "429" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [2, 2]> ("428", "neck.downsample_convs.1.conv.weight", "neck.fpn_convs.2.conv.bias")
   [Add_127] "430" = Add ("426", "429")
   [Conv_128] "431" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> ("428", "neck.pafpn_convs.0.conv.weight", "neck.pafpn_convs.0.conv.bias")
   [Conv_129] "432" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> ("430", "neck.pafpn_convs.1.conv.weight", "neck.pafpn_convs.1.conv.bias")
   [Conv_130] "589" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> ("424", "590", "591")
   [Relu_131] "435" = Relu ("589")
   [Conv_132] "592" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> ("435", "593", "594")
   [Relu_133] "438" = Relu ("592")
   val_0 = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> ("438", Conv_134_w, Conv_134_b)
   val_1 = Transpose <perm: ints = [0, 2, 3, 1]> (val_0)
   val_2 = Reshape (val_1, Conv_134_flat)
   val_3, boxes8, kps8 = Split <axis: int = 2> (val_2, Conv_134_split)
   scores8 = Sigmoid (val_3)
   [Conv_148] "595" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> ("431", "596", "597")
   [Relu_149] "455" = Relu ("595")
   [Conv_150] "598" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> ("455", "599", "600")
   [Relu_151] "458" = Relu ("598")
   val_4 = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> ("458", Conv_152_w, Conv_152_b)
   val_5 = Transpose <perm: ints = [0, 2, 3, 1]> (val_4)
   val_6 = Reshape (val_5, Conv_134_flat)
   val_7, boxes16, kps16 = Split <axis: int = 2> (val_6, Conv_134_split)
   scores16 = Sigmoid (val_7)
   [Conv_166] "601" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> ("432", "602", "603")
   [Relu_167] "475" = Relu ("601")
   [Conv_168] "604" = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> ("475", "605", "606")
   [Relu_169] "478" = Relu ("604")
   val_8 = Conv <dilations: ints = [1, 1], group: int = 1, kernel_shape: ints = [3, 3], pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> ("478", Conv_170_w, Conv_170_b)
   val_9 = Transpose <perm: ints = [0, 2, 3, 1]> (val_8)
   val_10 = Reshape (val_9, Conv_134_flat)
   val_11, boxes32, kps32 = Split <axis: int = 2> (val_10, Conv_134_split)
   scores32 = Sigmoid (val_11)
}

weights:
494 FLOAT[12,3,3,3] 48d69ebe596c
495 FLOAT[12] fd5bbbfd8ff8
497 FLOAT[12,12,3,3] 7986ed977630
498 FLOAT[12] 013e677b9005
500 FLOAT[24,12,3,3] 26412e802fc2
501 FLOAT[24] e599fc46b750
503 FLOAT[24,24,3,3] 7c522874c651
504 FLOAT[24] 7edded3c9b7f
506 FLOAT[24,24,3,3] 4a01546b58b9
507 FLOAT[24] 18b6a60ebb24
509 FLOAT[24,24,3,3] 547ed54633df
510 FLOAT[24] 4ec299b2391a
512 FLOAT[24,24,3,3] 2dd28d77426b
513 FLOAT[24] 5b36a7b624f6
515 FLOAT[24,24,3,3] d432ba5148df
516 FLOAT[24] 206913fcf8f5
518 FLOAT[24,24,3,3] b23abf84ebde
519 FLOAT[24] 39e5c7541fd8
521 FLOAT[48,24,3,3] 410e49c7cfb8
522 FLOAT[48] 894278055d43
524 FLOAT[48,48,3,3] 98f9e5acae6e
525 FLOAT[48] b5fe95a7ec4b
527 FLOAT[48,24,1,1] c5b3a2285ddf
528 FLOAT[48] 132011dbcc60
530 FLOAT[48,48,3,3] 65c6d5e08ee9
531 FLOAT[48] 2ab9708c21c2
533 FLOAT[48,48,3,3] bdcd7f3482b1
534 FLOAT[48] f3b67847e0a5
536 FLOAT[48,48,3,3] 2b946b56e92f
537 FLOAT[48] ad086696d5f7
539 FLOAT[48,48,3,3] 327c25a775f5
540 FLOAT[48] 621ba251905f
542 FLOAT[48,48,3,3] 07412a9c5e3b
543 FLOAT[48] 37268fd9fe40
545 FLOAT[48,48,3,3] ac52e079d8f7
546 FLOAT[48] 8511133dbe6b
548 FLOAT[48,48,3,3] b3250146cf62
549 FLOAT[48] 438d8ce27418
551 FLOAT[48,48,3,3] 8718c52650b8
552 FLOAT[48] 548b3782aa0b
554 FLOAT[48,48,3,3] 531481c88f00
555 FLOAT[48] cefecec2fb57
557 FLOAT[48,48,3,3] 66a2e479431d
558 FLOAT[48] 73dd25ebbfd9
560 FLOAT[48,48,1,1] f2993a809369
561 FLOAT[48] 6c080ff3f522
563 FLOAT[48,48,3,3] 7b256204fec5
564 FLOAT[48] 4cdec7712bc8
566 FLOAT[48,48,3,3] abec72a651c3
567 FLOAT[48] efac934cc2d4
569 FLOAT[48,48,3,3] ceafcd132ee1
570 FLOAT[48] a3d5a46a747b
572 FLOAT[48,48,3,3] f1657a7fb3ee
573 FLOAT[48] c668579ca944
575 FLOAT[80,48,3,3] d0962f308e7d
576 FLOAT[80] eceaa33e485f
578 FLOAT[80,80,3,3] ba43df368248
579 FLOAT[80] a46d8ee77919
581 FLOAT[80,48,1,1] c14546c03231
582 FLOAT[80] a83c6c127745
584 FLOAT[80,80,3,3] f0a4eba6e3c5
585 FLOAT[80] 1059d631e2f7
587 FLOAT[80,80,3,3] 25e3e5374f60
588 FLOAT[80] 0b35623fda7c
590 FLOAT[64,24,3,3] eb6f94790b08
591 FLOAT[64] b85581f0adf7
593 FLOAT[64,64,3,3] 6c626370d522
594 FLOAT[64] 3157ab081131
596 FLOAT[64,24,3,3] 041a9c281421
597 FLOAT[64] 7f5160093fff
599 FLOAT[64,64,3,3] 72b3c1e01b34
600 FLOAT[64] 3ed377b0be63
602 FLOAT[64,24,3,3] 5f9e09486cb8
603 FLOAT[64] 36b0fd5b23b2
605 FLOAT[64,64,3,3] 64e4b8374c70
606 FLOAT[64] 1c2ba4a0819b
Conv_134_b FLOAT[30] e8924a06f6d0
Conv_134_flat INT64[3] 9d186d7eddbd
Conv_134_split INT64[3] 0afded624a27
Conv_134_w FLOAT[30,64,3,3] a327faf33c3f
Conv_152_b FLOAT[30] 87093e6f573d
Conv_152_w FLOAT[30,64,3,3] 4d960aa3704b
Conv_170_b FLOAT[30] 4aae00b26cc9
Conv_170_w FLOAT[30,64,3,3] d868efba3df3
Resize_99_scales_2x FLOAT[4] aa5b3e0ef3e8
const_cast FLOAT[] 9fd754fbfd83
neck.downsample_convs.0.conv.bias FLOAT[24] 101727071629
neck.downsample_convs.0.conv.weight FLOAT[24,24,3,3] 092924a94037
neck.downsample_convs.1.conv.weight FLOAT[24,24,3,3] f97dc6f3b6d5
neck.fpn_convs.0.conv.bias FLOAT[24] ac12d9247140
neck.fpn_convs.0.conv.weight FLOAT[24,24,3,3] 9ff333b5c16e
neck.fpn_convs.1.conv.bias FLOAT[24] 5836b5c02165
neck.fpn_convs.1.conv.weight FLOAT[24,24,3,3] 1957ab17c4b1
neck.fpn_convs.2.conv.bias FLOAT[24] e3a89e52dfc1
neck.fpn_convs.2.conv.weight FLOAT[24,24,3,3] ec960dce896b
neck.lateral_convs.0.conv.bias FLOAT[24] fa58e06b6ede
neck.lateral_convs.0.conv.weight FLOAT[24,48,1,1] c5a9562ef13a
neck.lateral_convs.1.conv.bias FLOAT[24] 51c2345c4e30
neck.lateral_convs.1.conv.weight FLOAT[24,48,1,1] 328c5238ff67
neck.lateral_convs.2.conv.bias FLOAT[24] 0795c2926cf0
neck.lateral_convs.2.conv.weight FLOAT[24,80,1,1] fc4ad7208738
neck.pafpn_convs.0.conv.bias FLOAT[24] 03b4b5962b55
neck.pafpn_convs.0.conv.weight FLOAT[24,24,3,3] 8d90fbc6675a
neck.pafpn_convs.1.conv.bias FLOAT[24] a1f6bfa3e0aa
neck.pafpn_convs.1.conv.weight FLOAT[24,24,3,3] c2203b2f1023
