efficientdet_d0.yml

architecture: EfficientDet
max_iters: 281250
use_gpu: true
pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/EfficientNetB0_pretrained.tar
weights: output/efficientdet_d0/model_final
log_smooth_window: 20
snapshot_iter: 10000
metric: COCO
save_dir: output
num_classes: 81
use_ema: true
ema_decay: 0.9998

EfficientDet:
  backbone: EfficientNet
  fpn: BiFPN
  efficient_head: EfficientHead
  anchor_grid: AnchorGrid
  box_loss_weight: 50.

EfficientNet:
  # norm_type: sync_bn
  # TODO
  norm_type: bn
  scale: b0
  use_se: true

BiFPN:
  num_chan: 64
  repeat: 3
  levels: 5

EfficientHead:
  repeat: 3
  num_chan: 64
  prior_prob: 0.01
  num_anchors: 9
  gamma: 1.5
  alpha: 0.25
  delta: 0.1
  output_decoder:
    score_thresh: 0.05   # originally 0.
    nms_thresh: 0.5
    pre_nms_top_n: 1000  # originally 5000
    detections_per_im: 100
    nms_eta: 1.0

AnchorGrid:
  anchor_base_scale: 4
  num_scales: 3
  aspect_ratios: [[1, 1], [1.4, 0.7], [0.7, 1.4]]

LearningRate:
  base_lr: 0.16
  schedulers:
  - !CosineDecayWithSkip
    total_steps: 281250
    skip_steps: 938
  - !LinearWarmup
    start_factor: 0.05
    steps: 938

OptimizerBuilder:
  clip_grad_by_norm: 10.
  optimizer:
    momentum: 0.9
    type: Momentum
  regularizer:
    factor: 0.00004
    type: L2

TrainReader:
  inputs_def:
    fields: ['image', 'im_id', 'fg_num', 'gt_label', 'gt_target']
  dataset:
    !COCODataSet
    image_dir: train2017
    anno_path: annotations/instances_train2017.json
    dataset_dir: dataset/coco
  sample_transforms:
  - !DecodeImage
    to_rgb: true
  - !RandomFlipImage
    prob: 0.5
  - !NormalizeImage
    is_channel_first: false
    is_scale: true
    mean: [0.485,0.456,0.406]
    std: [0.229, 0.224,0.225]
  - !RandomScaledCrop
    target_dim: 512
    scale_range: [.1, 2.]
    interp: 1
  - !Permute
    to_bgr: false
    channel_first: true
  - !TargetAssign
    image_size: 512
  batch_size: 16
  shuffle: true
  worker_num: 32
  bufsize: 16
  use_process: true
  drop_empty: false

EvalReader:
  inputs_def:
    fields: ['image', 'im_info', 'im_id']
  dataset:
    !COCODataSet
    image_dir: val2017
    anno_path: annotations/instances_val2017.json
    dataset_dir: dataset/coco
  sample_transforms:
  - !DecodeImage
    to_rgb: true
    with_mixup: false
  - !NormalizeImage
    is_channel_first: false
    is_scale: true
    mean: [0.485,0.456,0.406]
    std: [0.229, 0.224,0.225]
  - !ResizeAndPad
    target_dim: 512
    interp: 1
  - !Permute
    channel_first: true
    to_bgr: false
  drop_empty: false
  batch_size: 16
  shuffle: false
  worker_num: 2

TestReader:
  inputs_def:
    fields: ['image', 'im_info', 'im_id']
    image_shape: [3, 512, 512]
  dataset:
    !ImageFolder
    anno_path: annotations/instances_val2017.json
  sample_transforms:
  - !DecodeImage
    to_rgb: true
    with_mixup: false
  - !NormalizeImage
    is_channel_first: false
    is_scale: true
    mean: [0.485,0.456,0.406]
    std: [0.229, 0.224,0.225]
  - !ResizeAndPad
    target_dim: 512
    interp: 1
  - !Permute
    channel_first: true
    to_bgr: false
  batch_size: 16
  shuffle: false