yolov3_darknet.yml

architecture: YOLOv3
use_gpu: true
max_iters: 500000
log_iter: 20
save_dir: output
snapshot_iter: 50000
metric: COCO
pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/DarkNet53_pretrained.tar
weights: output/yolov3_darknet/model_final
num_classes: 80
use_fine_grained_loss: false
load_static_weights: True

YOLOv3:
  anchor: AnchorYOLO
  backbone: DarkNet
  yolo_head: YOLOv3Head

DarkNet:
  depth: 53
  return_idx: [2, 3, 4]

YOLOv3Head:
  yolo_feat:
    name: YOLOFeat
    feat_in_list: [1024, 768, 384]
  ignore_thresh: 0.7
  downsample: 32
  label_smooth: true
  anchor_per_position: 3

AnchorYOLO:
  anchor_generator:
    name: AnchorGeneratorYOLO
    anchors: [10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326]
    anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
  anchor_post_process:
    name: BBoxPostProcessYOLO
    # decode -> clip
    yolo_box:
      name: YOLOBox
      conf_thresh: 0.005
      downsample_ratio: 32
      clip_bbox: True
    nms:
      name: MultiClassNMS
      keep_top_k: 100
      score_threshold: 0.01
      nms_threshold: 0.45
      nms_top_k: 1000
      normalized: false
      background_label: -1

LearningRate:
  base_lr: 0.001
  schedulers:
  - !PiecewiseDecay
    gamma: 0.1
    milestones:
    - 400000
    - 450000
  - !LinearWarmup
    start_factor: 0.
    steps: 4000

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

_READER_: 'yolov3_reader.yml'