architecture: YOLOv3
train_feed: YoloTrainFeed
eval_feed: YoloEvalFeed
test_feed: YoloTestFeed
use_gpu: true
max_iters: 120000
log_smooth_window: 20
save_dir: output
snapshot_iter: 2000
metric: COCO
pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/DarkNet53_pretrained.tar
weights: https://paddlemodels.bj.bcebos.com/object_detection/vehicle_yolov3_darknet.tar
num_classes: 6

YOLOv3:
  backbone: DarkNet
  yolo_head: YOLOv3Head

DarkNet:
  norm_type: sync_bn
  norm_decay: 0.
  depth: 53

YOLOv3Head:
  anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
  anchors: [[8, 9], [10, 23], [19, 15],
            [23, 33], [40, 25], [54, 50],
            [101, 80], [139, 145], [253, 224]]
  norm_decay: 0.
  ignore_thresh: 0.7
  label_smooth: false
  nms:
    background_label: -1
    keep_top_k: 100
    nms_threshold: 0.45
    nms_top_k: 400
    normalized: false
    score_threshold: 0.005

LearningRate:
  base_lr: 0.001
  schedulers:
  - !PiecewiseDecay
    gamma: 0.1
    milestones:
    - 60000
    - 80000
  - !LinearWarmup
    start_factor: 0.
    steps: 4000

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

YoloTrainFeed:
  batch_size: 8
  dataset:
    dataset_dir: dataset/vehicle
    annotation: annotations/instances_train2017.json
    image_dir: train2017
  num_workers: 8
  bufsize: 128
  use_process: true

YoloEvalFeed:
  batch_size: 8
  image_shape: [3, 608, 608]
  dataset:
    dataset_dir: dataset/vehicle
    annotation: annotations/instances_val2017.json
    image_dir: val2017

YoloTestFeed:
  batch_size: 1
  image_shape: [3, 608, 608]
  dataset:
    annotation: contrib/VehicleDetection/vehicle.json