# This config is an assembled config for ByteTrack MOT, used as eval/infer mode for MOT.
_BASE_: [
  '../bytetrack/detector/ppyoloe_crn_l_36e_640x640_mot17half.yml',
  '../bytetrack/_base_/mot17.yml',
  '../bytetrack/_base_/ppyoloe_mot_reader_640x640.yml'
]
weights: output/ocsort_ppyoloe/model_final
log_iter: 20
snapshot_epoch: 2

metric: MOT # eval/infer mode, set 'COCO' can be training mode
num_classes: 1

architecture: ByteTrack
pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/ppyoloe_crn_l_300e_coco.pdparams
ByteTrack:
  detector: YOLOv3 # PPYOLOe version
  reid: None
  tracker: OCSORTTracker
det_weights: https://bj.bcebos.com/v1/paddledet/models/mot/ppyoloe_crn_l_36e_640x640_mot17half.pdparams
reid_weights: None

YOLOv3:
  backbone: CSPResNet
  neck: CustomCSPPAN
  yolo_head: PPYOLOEHead
  post_process: ~

# Tracking requires higher quality boxes, so NMS score_threshold will be higher
PPYOLOEHead:
  fpn_strides: [32, 16, 8]
  grid_cell_scale: 5.0
  grid_cell_offset: 0.5
  static_assigner_epoch: -1 # 100
  use_varifocal_loss: True
  loss_weight: {class: 1.0, iou: 2.5, dfl: 0.5}
  static_assigner:
    name: ATSSAssigner
    topk: 9
  assigner:
    name: TaskAlignedAssigner
    topk: 13
    alpha: 1.0
    beta: 6.0
  nms:
    name: MultiClassNMS
    nms_top_k: 1000
    keep_top_k: 100
    score_threshold: 0.1 # 0.01 in original detector
    nms_threshold: 0.4 # 0.6 in original detector


OCSORTTracker:
  det_thresh: 0.4 # 0.6 in yolox ocsort
  max_age: 30
  min_hits: 3
  iou_threshold: 0.3
  delta_t: 3
  inertia: 0.2
  vertical_ratio: 0
  min_box_area: 0
  use_byte: False
  use_angle_cost: False


# MOTDataset for MOT evaluation and inference
EvalMOTDataset:
  !MOTImageFolder
    dataset_dir: dataset/mot
    data_root: MOT17/images/half
    keep_ori_im: True # set as True in DeepSORT and ByteTrack

TestMOTDataset:
  !MOTImageFolder
    dataset_dir: dataset/mot
    keep_ori_im: True # set True if save visualization images or video