faster_rcnn_x101_64x4d_fpn_1x.yml 2.7 KB
Newer Older
1 2 3 4 5 6
architecture: FasterRCNN
train_feed: FasterRCNNTrainFeed
eval_feed: FasterRCNNEvalFeed
test_feed: FasterRCNNTestFeed
max_iters: 180000
snapshot_iter: 10000
Y
Yang Zhang 已提交
7
use_gpu: true
8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112
log_smooth_window: 20
save_dir: output
pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_64x4d_pretrained.tar
weights: output/faster_rcnn_x101_64x4d_fpn_1x/model_final
metric: COCO

FasterRCNN:
  backbone: ResNeXt
  fpn: FPN
  rpn_head: FPNRPNHead
  roi_extractor: FPNRoIAlign
  bbox_head: BBoxHead
  bbox_assigner: BBoxAssigner

ResNeXt:
  depth: 101
  feature_maps: [2, 3, 4, 5]
  freeze_at: 2
  group_width: 4
  groups: 64
  norm_type: affine_channel

FPN:
  max_level: 6
  min_level: 2
  num_chan: 256
  spatial_scale: [0.03125, 0.0625, 0.125, 0.25]

FPNRPNHead:
  anchor_generator:
    anchor_sizes: [32, 64, 128, 256, 512]
    aspect_ratios: [0.5, 1.0, 2.0]
    stride: [16.0, 16.0]
    variance: [1.0, 1.0, 1.0, 1.0]
  anchor_start_size: 32
  max_level: 6
  min_level: 2
  num_chan: 256
  rpn_target_assign:
    rpn_batch_size_per_im: 256
    rpn_fg_fraction: 0.5
    rpn_negative_overlap: 0.3
    rpn_positive_overlap: 0.7
    rpn_straddle_thresh: 0.0
  train_proposal:
    min_size: 0.0
    nms_thresh: 0.7
    post_nms_top_n: 2000
    pre_nms_top_n: 2000
  test_proposal:
    min_size: 0.0
    nms_thresh: 0.7
    post_nms_top_n: 1000
    pre_nms_top_n: 1000

FPNRoIAlign:
  canconical_level: 4
  canonical_size: 224
  max_level: 5
  min_level: 2
  box_resolution: 7
  sampling_ratio: 2

BBoxAssigner:
  batch_size_per_im: 512
  bbox_reg_weights: [0.1, 0.1, 0.2, 0.2]
  bg_thresh_hi: 0.5
  bg_thresh_lo: 0.0
  fg_fraction: 0.25
  fg_thresh: 0.5
  num_classes: 81

BBoxHead:
  head: TwoFCHead
  nms:
    keep_top_k: 100
    nms_threshold: 0.5
    score_threshold: 0.05
  num_classes: 81

TwoFCHead:
  num_chan: 1024

LearningRate:
  base_lr: 0.01
  schedulers:
  - !PiecewiseDecay
    gamma: 0.1
    milestones: [120000, 160000]
  - !LinearWarmup
    start_factor: 0.3333333333333333
    steps: 500

OptimizerBuilder:
  optimizer:
    momentum: 0.9
    type: Momentum
  regularizer:
    factor: 0.0001
    type: L2

FasterRCNNTrainFeed:
  # batch size per device
  batch_size: 1
  dataset:
113
    dataset_dir: dataset/coco
114 115
    image_dir: train2017
    annotation: annotations/instances_train2017.json
Y
Yang Zhang 已提交
116 117 118
  batch_transforms:
  - !PadBatch
    pad_to_stride: 32
119 120 121 122 123
  num_workers: 2

FasterRCNNEvalFeed:
  batch_size: 1
  dataset:
124
    dataset_dir: dataset/coco
125 126
    annotation: annotations/instances_val2017.json
    image_dir: val2017
Y
Yang Zhang 已提交
127 128 129
  batch_transforms:
  - !PadBatch
    pad_to_stride: 32
130 131 132 133
  num_workers: 2

FasterRCNNTestFeed:
  batch_size: 1
Y
Yang Zhang 已提交
134 135
  dataset:
    annotation: annotations/instances_val2017.json
136 137 138 139
  batch_transforms:
  - !PadBatch
    pad_to_stride: 32
  num_workers: 2