architecture: YOLOv3
use_gpu: true
max_iters: 70000
log_smooth_window: 20
save_dir: output
snapshot_iter: 2000
metric: VOC
map_type: 11point
pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet34_pretrained.tar
weights: output/yolov3_r34_voc/model_final
num_classes: 20
use_fine_grained_loss: false

YOLOv3:
  backbone: ResNet
  yolo_head: YOLOv3Head

ResNet:
  norm_type: sync_bn
  freeze_at: 0
  freeze_norm: false
  norm_decay: 0.
  depth: 34
  feature_maps: [3, 4, 5]

YOLOv3Head:
  anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
  anchors: [[10, 13], [16, 30], [33, 23],
            [30, 61], [62, 45], [59, 119],
            [116, 90], [156, 198], [373, 326]]
  norm_decay: 0.
  yolo_loss: YOLOv3Loss
  nms:
    background_label: -1
    keep_top_k: 100
    nms_threshold: 0.45
    nms_top_k: 1000
    normalized: false
    score_threshold: 0.01

YOLOv3Loss:
  # batch_size here is only used for fine grained loss, not used
  # for training batch_size setting, training batch_size setting
  # is in configs/yolov3_reader.yml TrainReader.batch_size, batch
  # size here should be set as same value as TrainReader.batch_size
  batch_size: 8
  ignore_thresh: 0.7
  label_smooth: false

LearningRate:
  base_lr: 0.001
  schedulers:
  - !PiecewiseDecay
    gamma: 0.1
    milestones:
    - 55000
    - 62000
  - !LinearWarmup
    start_factor: 0.
    steps: 1000

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

_READER_: 'yolov3_reader.yml'
TrainReader:
  dataset:
    !VOCDataSet
    dataset_dir: dataset/voc
    anno_path: trainval.txt
    use_default_label: true
    with_background: false

EvalReader:
  inputs_def:
    fields: ['image', 'im_size', 'im_id', 'gt_bbox', 'gt_class', 'is_difficult']
    num_max_boxes: 50
  dataset:
    !VOCDataSet
    dataset_dir: dataset/voc
    anno_path: test.txt
    use_default_label: true
    with_background: false

TestReader:
  dataset:
    !ImageFolder
    use_default_label: true
    with_background: false