Hi, Can you release your training model config file for reference? Because I use netron check my model is only one output. But your excellent model have six output. Have you done any other processing on the model? Following text is my training config file.

# nanodet-EfficientNet-Lite1_416
save_dir: workspace/RepVGG-A0-416
model:
  arch:
    name: OneStageDetector
    backbone:
      name: RepVGG
      arch: A0
      out_stages: [2,3,4]
      activation: ReLU
      last_channel: 512
      deploy: False
    fpn:
      name: PAN
      in_channels: [96, 192, 512]
      out_channels: 128
      start_level: 0
      num_outs: 3
    head:
      name: NanoDetHead
      num_classes: 1
      conv_type: Conv
      input_channel: 128
      feat_channels: 128
      stacked_convs: 2
      activation: ReLU
      share_cls_reg: True
      octave_base_scale: 8
      scales_per_octave: 1
      strides: [8, 16, 32]
      reg_max: 10
      norm_cfg:
        type: BN
      loss:
        loss_qfl:
          name: QualityFocalLoss
          use_sigmoid: True
          beta: 2.0
          loss_weight: 1.0
        loss_dfl:
          name: DistributionFocalLoss
          loss_weight: 0.25
        loss_bbox:
          name: GIoULoss
          loss_weight: 2.0
data:
  train:
    name: CocoDataset
    img_path: /nfs/Workspace/dataset/mscoco/images/train2017
    ann_path: /nfs/Workspace/dataset/mscoco/annotations/annotations/instances_train2017_person.json
    input_size: [416,416] #[w,h]
    keep_ratio: True
    pipeline:
      perspective: 0.0
      scale: [0.5, 1.5]
      stretch: [[1, 1], [1, 1]]
      rotation: 0
      shear: 0
      translate: 0.2
      flip: 0.5
      brightness: 0.2
      contrast: [0.6, 1.4]
      saturation: [0.5, 1.2]
      normalize: [[103.53, 116.28, 123.675], [57.375, 57.12, 58.395]]
  val:
    name: CocoDataset
    img_path: /nfs/Workspace/dataset/mscoco/images/val2017
    ann_path: /nfs/Workspace/dataset/mscoco/annotations/annotations/instances_val2017_person.json
    input_size: [416,416] #[w,h]
    keep_ratio: True
    pipeline:
      normalize: [[103.53, 116.28, 123.675], [57.375, 57.12, 58.395]]
device:
  gpu_ids: [0]
  workers_per_gpu: 1
  batchsize_per_gpu: 100
schedule:
#  resume:
#  load_model: YOUR_MODEL_PATH
  optimizer:
    name: SGD
    lr: 0.07
    momentum: 0.9
    weight_decay: 0.0001
  warmup:
    name: linear
    steps: 500
    ratio: 0.01
  total_epochs: 170
  lr_schedule:
    name: MultiStepLR
    milestones: [130,150,160,165]
    gamma: 0.1
  val_intervals: 5
evaluator:
  name: CocoDetectionEvaluator
  save_key: mAP

log:
  interval: 10

class_names: ['person']

And this is my model architecture output

Here is the model architecture output you provided

Please help! Thanks a lot!

when i use cv2.dnn.readNet() to read the onnx model, i have a question (expected: 'shapes.depth() == CV_32S') where 'shapes.depth()' is 5(CV_32FC1) must be equal to 'CV_32S' is 4(CV_32SC1) in void_cdecl cv::dnn::dnn4_v20200609::ONNXImporter::populateNet(class cv::dnn::dnn4_v20200609::Net), file D:\opencv_test\newbuild\opencv-master\modules\dnn\src\onnx\innx_importer.cpp, line 1442

[ INFO:0] global C:\jenkins\workspace\OpenCV\OpenVINO\2021.2\build\windows\opencv\modules\dnn\src\onnx\onnx_importer.cpp (395) cv::dnn::dnn4_v20201117::ONNXImporter::populateNet DNN/ONNX: loading ONNX v6 model produced by 'pytorch':1.7. Number of nodes = 335, inputs = 1, outputs = 6