solov2_r50_fpn_1x.yml

architecture: SOLOv2
use_gpu: true
max_iters: 90000
snapshot_iter: 10000
log_smooth_window: 20
save_dir: output
pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_cos_pretrained.tar
metric: COCO
weights: output/solov2_r50_fpn_1x/model_final
num_classes: 81

SOLOv2:
  backbone: ResNet
  fpn: FPN
  bbox_head: SOLOv2Head
  mask_head: SOLOv2MaskHead

ResNet:
  depth: 50
  feature_maps: [2, 3, 4, 5]
  freeze_at: 2
  norm_type: bn

FPN:
  max_level: 6
  min_level: 2
  num_chan: 256
  spatial_scale: [0.03125, 0.0625, 0.125, 0.25]
  reverse_out: True

SOLOv2Head:
  seg_feat_channels: 512
  stacked_convs: 4
  num_grids: [40, 36, 24, 16, 12]
  kernel_out_channels: 256
  solov2_loss: SOLOv2Loss
  mask_nms: MaskMatrixNMS

SOLOv2MaskHead:
  in_channels: 128
  out_channels: 256
  start_level: 0
  end_level: 3

SOLOv2Loss:
  ins_loss_weight: 3.0
  focal_loss_gamma: 2.0
  focal_loss_alpha: 0.25

MaskMatrixNMS:
  pre_nms_top_n: 500
  post_nms_top_n: 100

LearningRate:
  base_lr: 0.01
  schedulers:
  - !PiecewiseDecay
    gamma: 0.1
    milestones: [60000, 80000]
  - !LinearWarmup
    start_factor: 0.
    steps: 1000

OptimizerBuilder:
  optimizer:
    momentum: 0.9
    type: Momentum
  regularizer:
    factor: 0.0001
    type: L2

_READER_: 'solov2_reader.yml'