# This config is an assembled config for ByteTrack MOT, used as eval/infer mode for MOT.
_BASE_: [
  'detector/yolox_x_24e_800x1440_ht21.yml',
  '_base_/ht21.yml',
  '_base_/yolox_mot_reader_800x1440.yml'
]
weights: output/bytetrack_yolox_ht21/model_final
log_iter: 20
snapshot_epoch: 2

metric: MOT # eval/infer mode
num_classes: 1

architecture: ByteTrack
pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/yolox_x_300e_coco.pdparams
ByteTrack:
  detector: YOLOX
  reid: None
  tracker: JDETracker
det_weights: https://bj.bcebos.com/v1/paddledet/models/mot/yolox_x_24e_800x1440_ht21.pdparams
reid_weights: None

depth_mult: 1.33
width_mult: 1.25

YOLOX:
  backbone: CSPDarkNet
  neck: YOLOCSPPAN
  head: YOLOXHead
  input_size: [800, 1440]
  size_stride: 32
  size_range: [18, 22] # multi-scale range [576*1024 ~ 800*1440], w/h ratio=1.8

CSPDarkNet:
  arch: "X"
  return_idx: [2, 3, 4]
  depthwise: False

YOLOCSPPAN:
  depthwise: False

# Tracking requires higher quality boxes, so NMS score_threshold will be higher
YOLOXHead:
  l1_epoch: 20
  depthwise: False
  loss_weight: {cls: 1.0, obj: 1.0, iou: 5.0, l1: 1.0}
  assigner:
    name: SimOTAAssigner
    candidate_topk: 10
    use_vfl: False
  nms:
    name: MultiClassNMS
    nms_top_k: 30000
    keep_top_k: 1000
    score_threshold: 0.01
    nms_threshold: 0.7
    # For speed while keep high mAP, you can modify 'nms_top_k' to 1000 and 'keep_top_k' to 100, the mAP will drop about 0.1%.
    # For high speed demo, you can modify 'score_threshold' to 0.25 and 'nms_threshold' to 0.45, but the mAP will drop a lot.


# BYTETracker
JDETracker:
  use_byte: True
  match_thres: 0.9
  conf_thres: 0.7
  low_conf_thres: 0.1
  min_box_area: 0
  vertical_ratio: 0 # 1.6 for pedestrian