ssd_vgg16_512.yml

architecture: SSD
train_feed: SSDTrainFeed
eval_feed: SSDEvalFeed
test_feed: SSDTestFeed
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_512/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, -1]
  extra_block_filters: [[256, 512, 1, 2, 3], [128, 256, 1, 2, 3], [128, 256, 1, 2, 3], [128, 256, 1, 2, 3], [128, 256, 1, 1, 4]]


MultiBoxHead:
  base_size: 512
  aspect_ratios: [[2.], [2., 3.], [2., 3.], [2., 3.], [2., 3.], [2.], [2.]]
  min_ratio: 15
  max_ratio: 90
  min_sizes: [20.0, 51.0, 133.0, 215.0, 296.0, 378.0, 460.0]
  max_sizes: [51.0, 133.0, 215.0, 296.0, 378.0, 460.0, 542.0]
  steps: [8, 16, 32, 64, 128, 256, 512]
  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

SSDTrainFeed:
  batch_size: 8
  dataset:
    dataset_dir: dataset/coco
    annotation: annotations/instances_train2017.json
    image_dir: train2017
  image_shape: [3, 512, 512]
  sample_transforms:
  - !DecodeImage
    to_rgb: true
    with_mixup: false
  - !NormalizeBox {}
  - !RandomDistort
    brightness_lower: 0.875
    brightness_upper: 1.125
    is_order: true
  - !ExpandImage
    max_ratio: 4
    mean: [104, 117, 123]
    prob: 0.5
  - !CropImage
    avoid_no_bbox: true
    batch_sampler:
    - [1, 1, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]
    - [1, 50, 0.3, 1.0, 0.5, 2.0, 0.1, 0.0]
    - [1, 50, 0.3, 1.0, 0.5, 2.0, 0.3, 0.0]
    - [1, 50, 0.3, 1.0, 0.5, 2.0, 0.5, 0.0]
    - [1, 50, 0.3, 1.0, 0.5, 2.0, 0.7, 0.0]
    - [1, 50, 0.3, 1.0, 0.5, 2.0, 0.9, 0.0]
    - [1, 50, 0.3, 1.0, 0.5, 2.0, 0.0, 1.0]
    satisfy_all: false
  - !ResizeImage
    interp: 1
    target_size: 512
    use_cv2: false
  - !RandomFlipImage
    is_normalized: true
  - !Permute
    to_bgr: false
  - !NormalizeImage
    is_scale: false
    mean: [104, 117, 123]
    std: [1, 1, 1]

SSDEvalFeed:
  batch_size: 8
  dataset:
    dataset_dir: dataset/coco
    annotation: annotations/instances_val2017.json
    image_dir: val2017
  drop_last: false
  image_shape: [3, 512, 512]
  sample_transforms:
  - !DecodeImage
    to_rgb: true
    with_mixup: false
  - !ResizeImage
    interp: 1
    target_size: 512
    use_cv2: false
  - !Permute
    to_bgr: false
  - !NormalizeImage
    is_scale: false
    mean: [104, 117, 123]
    std: [1, 1, 1]

SSDTestFeed:
  batch_size: 1
  dataset:
    annotation: dataset/coco/annotations/instances_val2017.json
  image_shape: [3, 512, 512]
  sample_transforms:
  - !DecodeImage
    to_rgb: true
    with_mixup: false
  - !ResizeImage
    interp: 1
    max_size: 0
    target_size: 512
    use_cv2: false
  - !Permute
    to_bgr: false
  - !NormalizeImage
    is_scale: false
    mean: [104, 117, 123]
    std: [1, 1, 1]