ssd_vgg16_300.yml

architecture: SSD
use_gpu: true
max_iters: 400000
snapshot_iter: 10000
log_smooth_window: 20
log_iter: 20
metric: COCO
pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/VGG16_caffe_pretrained.tar
save_dir: output
weights: output/ssd_vgg16_300/model_final
num_classes: 81

SSD:
  backbone: VGG
  multi_box_head: MultiBoxHead
  output_decoder:
    background_label: 0
    keep_top_k: 200
    nms_eta: 1.0
    nms_threshold: 0.45
    nms_top_k: 400
    score_threshold: 0.01

VGG:
  depth: 16
  with_extra_blocks: true
  normalizations: [20., -1, -1, -1, -1, -1]

MultiBoxHead:
  base_size: 300
  aspect_ratios: [[2.], [2., 3.], [2., 3.], [2., 3.], [2.], [2.]]
  min_ratio: 15
  max_ratio: 90
  min_sizes: [30.0, 60.0, 111.0, 162.0, 213.0, 264.0]
  max_sizes: [60.0, 111.0, 162.0, 213.0, 264.0, 315.0]
  steps: [8, 16, 32, 64, 100, 300]
  offset: 0.5
  flip: true
  kernel_size: 3
  pad: 1

LearningRate:
  base_lr: 0.001
  schedulers:
  - !PiecewiseDecay
    gamma: 0.1
    milestones: [280000, 360000]
  - !LinearWarmup
    start_factor: 0.3333333333333333
    steps: 500

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

TrainReader:
  inputs_def:
    image_shape: [3, 300, 300]
    fields: ['image', 'gt_bbox', 'gt_class']
  dataset:
    !COCODataSet
    image_dir: val2017
    anno_path: annotations/instances_val2017.json
    dataset_dir: dataset/coco
  sample_transforms:
  - !DecodeImage
    to_rgb: true
  - !RandomDistort
    brightness_lower: 0.875
    brightness_upper: 1.125
    is_order: true
  - !RandomExpand
    fill_value: [104, 117, 123]
  - !RandomCrop
    allow_no_crop: true
  - !NormalizeBox {}
  - !ResizeImage
    interp: 1
    target_size: 300
    use_cv2: false
  - !RandomFlipImage
    is_normalized: true
  - !Permute
    to_bgr: false
  - !NormalizeImage
    is_scale: false
    mean: [104, 117, 123]
    std: [1, 1, 1]
  batch_size: 8
  shuffle: true
  worker_num: 8
  bufsize: 32
  use_process: true
  drop_empty: true

EvalReader:
  inputs_def:
    image_shape: [3, 300, 300]
    fields: ['image', 'gt_bbox', 'gt_class', 'im_shape', 'im_id', 'is_difficult']
  dataset:
    !COCODataSet
    image_dir: val2017
    anno_path: annotations/instances_val2017.json
    dataset_dir: dataset/coco
  sample_transforms:
  - !DecodeImage
    to_rgb: true
    with_mixup: false
  - !NormalizeBox {}
  - !ResizeImage
    interp: 1
    target_size: 300
    use_cv2: false
  - !Permute
    to_bgr: false
  - !NormalizeImage
    is_scale: false
    mean: [104, 117, 123]
    std: [1, 1, 1]
  batch_size: 16
  worker_num: 8
  bufsize: 32

TestReader:
  inputs_def:
    image_shape: [3,300,300]
    fields: ['image', 'im_id', 'im_shape']
  dataset:
    !ImageFolder
    anno_path: annotations/instances_val2017.json
  sample_transforms:
  - !DecodeImage
    to_rgb: true
    with_mixup: false
  - !ResizeImage
    interp: 1
    max_size: 0
    target_size: 300
    use_cv2: false
  - !Permute
    to_bgr: false
  - !NormalizeImage
    is_scale: false
    mean: [104, 117, 123]
    std: [1, 1, 1]
  batch_size: 1