super-gradients/src/super_gradients/recipes/arch_params/ppyoloe_arch_params.yaml

depth_mult:
width_mult:
num_classes: 80

backbone:
  layers: [ 3, 6, 6, 3 ]                # Backbone's structure
  channels: [ 64, 128, 256, 512, 1024 ] # Number of outputs channels for stem and consecutive feature maps
  activation: silu
  return_idx: [ 1, 2, 3 ]               # Indexes of feature maps to output, indiced 1,2,3 correspond to feature maps of stride 8,16,32
  use_large_stem: True                  # If True, uses 3 conv+bn+act instead of 2 in stem blocks
  use_alpha: False                      # If True, enables additional learnable weighting parameter for 1x1 branch in RepVGGBlock
  pretrained_weights:

neck:
  in_channels: [256, 512, 1024]
  out_channels: [768, 384, 192]
  activation: silu
  block_num: 3
  stage_num: 1
  spp: True

head:
  in_channels: [768, 384, 192]
  activation: silu

  fpn_strides: [32, 16, 8]
  grid_cell_scale: 5.0
  grid_cell_offset: 0.5
  reg_max: 16 # Number of bins for size prediction

  eval_size:    # Size of the image for evaluation. Setting this value can be beneficial for inference speed since anchors will not be regenerated for each forward call.