DocumentAIKit/configs/picodet/application/pedestrian_detection/picodet_s_192_lcnet_pedestrian.yml

use_gpu: true
use_xpu: false
log_iter: 20
save_dir: output
snapshot_epoch: 1
print_flops: false

# Exporting the model
export:
  post_process: True  # Whether post-processing is included in the network when export model.
  nms: True           # Whether NMS is included in the network when export model.
  benchmark: False    # It is used to testing model performance, if set `True`, post-process and NMS will not be exported.

metric: COCO
num_classes: 1

architecture: PicoDet
pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_75_pretrained.pdparams
weights: output/picodet_s_192_lcnet_pedestrian/best_model
find_unused_parameters: True
use_ema: true
epoch: 300
snapshot_epoch: 10

PicoDet:
  backbone: LCNet
  neck: LCPAN
  head: PicoHeadV2

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

LCPAN:
  out_channels: 96
  use_depthwise: True
  num_features: 4

PicoHeadV2:
  conv_feat:
    name: PicoFeat
    feat_in: 96
    feat_out: 96
    num_convs: 2
    num_fpn_stride: 4
    norm_type: bn
    share_cls_reg: True
    use_se: True
  feat_in_chan: 96
  fpn_stride: [8, 16, 32, 64]
  prior_prob: 0.01
  reg_max: 7
  cell_offset: 0.5
  grid_cell_scale: 5.0
  static_assigner_epoch: 100
  use_align_head: True
  static_assigner:
    name: ATSSAssigner
    topk: 4
    force_gt_matching: False
  assigner:
    name: TaskAlignedAssigner
    topk: 13
    alpha: 1.0
    beta: 6.0
  loss_class:
    name: VarifocalLoss
    use_sigmoid: False
    iou_weighted: True
    loss_weight: 1.0
  loss_dfl:
    name: DistributionFocalLoss
    loss_weight: 0.5
  loss_bbox:
    name: GIoULoss
    loss_weight: 2.5
  nms:
    name: MultiClassNMS
    nms_top_k: 1000
    keep_top_k: 100
    score_threshold: 0.025
    nms_threshold: 0.6

LearningRate:
  base_lr: 0.32
  schedulers:
  - !CosineDecay
    max_epochs: 300
  - !LinearWarmup
    start_factor: 0.1
    steps: 300

OptimizerBuilder:
  optimizer:
    momentum: 0.9
    type: Momentum
  regularizer:
    factor: 0.00004
    type: L2

worker_num: 6
eval_height: &eval_height 192
eval_width: &eval_width 192
eval_size: &eval_size [*eval_height, *eval_width]

TrainReader:
  sample_transforms:
  - Decode: {}
  - RandomCrop: {}
  - RandomFlip: {prob: 0.5}
  - RandomDistort: {}
  batch_transforms:
  - BatchRandomResize: {target_size: [128, 160, 192, 224, 256], random_size: True, random_interp: True, keep_ratio: False}
  - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
  - Permute: {}
  - PadGT: {}
  batch_size: 64
  shuffle: true
  drop_last: true


EvalReader:
  sample_transforms:
  - Decode: {}
  - Resize: {interp: 2, target_size: *eval_size, keep_ratio: False}
  - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
  - 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: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
  - Permute: {}
  batch_size: 1


TrainDataset:
  !COCODataSet
    image_dir: ""
    anno_path: aic_coco_train_cocoformat.json
    dataset_dir: dataset
    data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']

EvalDataset:
  !COCODataSet
    image_dir: val2017
    anno_path: annotations/instances_val2017.json
    dataset_dir: dataset/coco

TestDataset:
  !ImageFolder
    anno_path: annotations/instances_val2017.json # also support txt (like VOC's label_list.txt)
    dataset_dir: dataset/coco # if set, anno_path will be 'dataset_dir/anno_path'