picodet_s_416_coco_npu.yml

_BASE_: [
  '../datasets/coco_detection.yml',
  '../runtime.yml',
  '_base_/picodet_v2.yml',
  '_base_/optimizer_300e.yml',
]

pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_75_pretrained.pdparams
weights: output/picodet_s_416_coco/best_model
find_unused_parameters: True
keep_best_weight: True
use_ema: True
epoch: 300
snapshot_epoch: 10

PicoDet:
  backbone: LCNet
  neck: CSPPAN
  head: PicoHeadV2

LCNet:
  scale: 0.75
  feature_maps: [3, 4, 5]
  act: relu6

CSPPAN:
  out_channels: 96
  use_depthwise: True
  num_csp_blocks: 1
  num_features: 4
  act: relu6

PicoHeadV2:
  conv_feat:
    name: PicoFeat
    feat_in: 96
    feat_out: 96
    num_convs: 4
    num_fpn_stride: 4
    norm_type: bn
    share_cls_reg: True
    use_se: True
    act: relu6
  feat_in_chan: 96
  act: relu6

LearningRate:
  base_lr: 0.2
  schedulers:
  - !CosineDecay
    max_epochs: 300
    min_lr_ratio: 0.08
    last_plateau_epochs: 30
  - !ExpWarmup
    epochs: 2

worker_num: 6
eval_height: &eval_height 416
eval_width: &eval_width 416
eval_size: &eval_size [*eval_height, *eval_width]

TrainReader:
  sample_transforms:
  - Decode: {}
  - Mosaic:
      prob: 0.6
      input_dim: [640, 640]
      degrees: [-10, 10]
      scale: [0.1, 2.0]
      shear: [-2, 2]
      translate: [-0.1, 0.1]
      enable_mixup: True
  - AugmentHSV: {is_bgr: False, hgain: 5, sgain: 30, vgain: 30}
  - RandomFlip: {prob: 0.5}
  batch_transforms:
  - BatchRandomResize: {target_size: [320, 352, 384, 416, 448, 480, 512], random_size: True, random_interp: True, keep_ratio: False}
  - NormalizeImage: {mean: [0, 0, 0], std: [1, 1, 1], is_scale: True}
  - Permute: {}
  - PadGT: {}
  batch_size: 40
  shuffle: true
  drop_last: true
  mosaic_epoch: 180


EvalReader:
  sample_transforms:
  - Decode: {}
  - Resize: {interp: 2, target_size: *eval_size, keep_ratio: False}
  - NormalizeImage: {mean: [0, 0, 0], std: [1, 1, 1], is_scale: True}
  - Permute: {}
  batch_transforms:
  - PadBatch: {pad_to_stride: 32}
  batch_size: 8
  shuffle: false


TestReader:
  inputs_def:
    image_shape: [1, 3, *eval_height, *eval_width]
  sample_transforms:
  - Decode: {}
  - Resize: {interp: 2, target_size: *eval_size, keep_ratio: False}
  - NormalizeImage: {mean: [0, 0, 0], std: [1, 1, 1], is_scale: True}
  - Permute: {}
  batch_size: 1