_BASE_: [
  '../fairmot/fairmot_dla34_30e_1088x608.yml',
  '../../datasets/mcmot.yml'
]
metric: MCMOT
num_classes: 4

# for MCMOT training
TrainDataset:
  !MCMOTDataSet
    dataset_dir: dataset/mot
    image_lists: ['visdrone_mcmot_vehicle.train']
    data_fields: ['image', 'gt_bbox', 'gt_class', 'gt_ide']
    label_list: label_list.txt

EvalMOTDataset:
  !MOTImageFolder
    dataset_dir: dataset/mot
    data_root: visdrone_mcmot_vehicle/images/val
    keep_ori_im: False # set True if save visualization images or video, or used in DeepSORT
    anno_path: dataset/mot/visdrone_mcmot_vehicle/label_list.txt

# for MCMOT video inference
TestMOTDataset:
  !MOTImageFolder
    dataset_dir: dataset/mot
    keep_ori_im: True # set True if save visualization images or video
    anno_path: dataset/mot/visdrone_mcmot_vehicle/label_list.txt


pretrain_weights: https://paddledet.bj.bcebos.com/models/centernet_dla34_140e_coco.pdparams

FairMOT:
  detector: CenterNet
  reid: FairMOTEmbeddingHead
  loss: FairMOTLoss
  tracker: JDETracker # multi-class tracker

CenterNetHead:
  regress_ltrb: False

CenterNetPostProcess:
  regress_ltrb: False
  max_per_img: 200

JDETracker:
  min_box_area: 0
  vertical_ratio: 0 # no need to filter bboxes according to w/h
  use_byte: True
  match_thres: 0.8
  conf_thres: 0.4
  low_conf_thres: 0.2

weights: output/mcfairmot_dla34_30e_1088x608_visdrone_vehicle_bytetracker/model_final

epoch: 30
LearningRate:
  base_lr: 0.0005
  schedulers:
  - !PiecewiseDecay
    gamma: 0.1
    milestones: [10, 20]
    use_warmup: False

OptimizerBuilder:
  optimizer:
    type: Adam
  regularizer: NULL