architecture: YOLOv5
use_gpu: true
max_iters: 85000
log_smooth_window: 1
save_dir: output
snapshot_iter: 5000
metric: COCO
pretrain_weights: https://paddlemodels.bj.bcebos.com/object_detection/ResNet50_vd_dcn_db_obj365_pretrained.tar
weights: output/yolov3_r50vd_dcn_db_iouaware_obj365_pretrained_coco/model_final
use_fine_grained_loss: false
num_classes: 80

YOLOv5:
  backbone: CSPYolo
  yolo_head: YOLOv5Head
  use_fine_grained_loss: false

CSPYolo:
  depth_multiple: 1.33
  width_multiple: 1.25
  act: 'mish'
  yolov5: false
  save: [22, 26, 30]
  weight_prefix_name: 'model'
  layers: [
   [-1, 1, 'Conv', [32, 3, 1]],  # 0
   [-1, 1, 'Conv', [64, 3, 2]],  # 1-P1/2
   [-1, 1, 'Bottleneck', [64]],
   [-1, 1, 'Conv', [128, 3, 2]],  # 3-P2/4
   [-1, 2, 'BottleneckCSP', [128]],
   [-1, 1, 'Conv', [256, 3, 2]],  # 5-P3/8
   [-1, 8, 'BottleneckCSP', [256]],
   [-1, 1, 'Conv', [512, 3, 2]],  # 7-P4/16
   [-1, 8, 'BottleneckCSP', [512]],
   [-1, 1, 'Conv', [1024, 3, 2]], # 9-P5/32
   [-1, 4, 'BottleneckCSP', [1024]],  # 10
  ]
  neck: [
   [-1, 1, 'SPPCSP', [512]], # 11
   [-1, 1, 'Conv', [256, 1, 1]],
   [-1, 1, 'Upsample', ['None', 2, 'nearest']],
   [8, 1, 'Conv', [256, 1, 1]], # route backbone P4
   [[-1, -2], 1, 'Concat', [1]],
   [-1, 2, 'BottleneckCSP2', [256]], # 16 
   [-1, 1, 'Conv', [128, 1, 1]],
   [-1, 1, 'Upsample', ['None', 2, 'nearest']],
   [6, 1, 'Conv', [128, 1, 1]], # route backbone P3
   [[-1, -2], 1, 'Concat', [1]],
   [-1, 2, 'BottleneckCSP2', [128]], # 21
   [-1, 1, 'Conv', [256, 3, 1]],
   [-2, 1, 'Conv', [256, 3, 2]],
   [[-1, 16], 1, 'Concat', [1]],  # cat
   [-1, 2, 'BottleneckCSP2', [256]], # 25
   [-1, 1, 'Conv', [512, 3, 1]],
   [-2, 1, 'Conv', [512, 3, 2]],
   [[-1, 11], 1, 'Concat', [1]],  # cat
   [-1, 2, 'BottleneckCSP2', [512]], # 29
   [-1, 1, 'Conv', [1024, 3, 1]]
  ]

YOLOv5Head:
  anchors: [[12, 16], [19, 36], [40, 28], [36, 75], [76, 55],
            [72, 146], [142, 110], [192, 243], [459, 401]]
  anchor_masks: [[0, 1, 2], [3, 4, 5], [6, 7, 8]]
  yolo_loss: YOLOv3Loss
  stride: [8, 16, 32]
  start: 31
  nms:
    background_label: -1
    keep_top_k: 300
    nms_threshold: 0.65 #0.45
    nms_top_k: -1
    normalized: false
    score_threshold: 0.001 #0.001
  weight_prefix_name: 'model'


YOLOv3Loss:
  batch_size: 4
  ignore_thresh: 0.7
  label_smooth: false
  use_fine_grained_loss: false

LearningRate:
  base_lr: 0.001
  schedulers:
  - !PiecewiseDecay
    gamma: 0.1
    milestones:
    - 55000
    - 75000
  - !LinearWarmup
    start_factor: 0.
    steps: 4000

OptimizerBuilder:
  optimizer:
    momentum: 0.9
    type: Momentum
  regularizer:
    factor: 0.0005
    type: L2

_READER_: 'yolov5_reader.yml'