gfl_r101vd_fpn_mstrain_2x_coco.yml

_BASE_: [
  '../datasets/coco_detection.yml',
  '../runtime.yml',
  '_base_/gfl_r50_fpn.yml',
  '_base_/optimizer_1x.yml',
  '_base_/gfl_reader.yml',
]

pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet101_vd_pretrained.pdparams
weights: output/gfl_r101vd_fpn_mstrain_2x_coco/model_final
find_unused_parameters: True
use_ema: true
ema_decay: 0.9998

ResNet:
  depth: 101
  variant: d
  norm_type: bn
  freeze_at: 0
  return_idx: [1,2,3]
  num_stages: 4

epoch: 24

LearningRate:
  base_lr: 0.01
  schedulers:
  - !PiecewiseDecay
    gamma: 0.1
    milestones: [16, 22]
  - !LinearWarmup
    start_factor: 0.001
    steps: 500

TrainReader:
  sample_transforms:
  - Decode: {}
  - RandomResize: {target_size: [[480, 1333], [512, 1333], [544, 1333], [576, 1333], [608, 1333], [640, 1333], [672, 1333], [704, 1333], [736, 1333], [768, 1333], [800, 1333]], interp: 2, keep_ratio: True}
  - RandomFlip: {prob: 0.5}
  - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
  - Permute: {}
  batch_transforms:
  - PadBatch: {pad_to_stride: 32}
  - Gt2GFLTarget:
      downsample_ratios: [8, 16, 32, 64, 128]
      grid_cell_scale: 8