yolov5.yml

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: 'hard_swish'
  weight_prefix_name: 'model'

YOLOv5Head:
  anchors: [[10, 13], [16, 30], [33, 23], [30, 61], [62, 45],
            [59, 119], [116, 90], [156, 198], [373, 326]]
  anchor_masks: [[0, 1, 2], [3, 4, 5], [6, 7, 8]]
  yolo_loss: YOLOv3Loss
  stride: [8, 16, 32]
  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'