# This config is an assembled config for ByteTrack MOT, used as eval/infer mode for MOT.
_BASE_: [
  '../../../yolox/yolox_x_300e_coco.yml',
  '../_base_/mix_det.yml',
]
weights: output/yolox_x_24e_800x1440_mix_det/model_final
log_iter: 20
snapshot_epoch: 2

# schedule configuration for fine-tuning
epoch: 24
LearningRate:
  base_lr: 0.00075 # fintune
  schedulers:
  - !CosineDecay
    max_epochs: 24
    min_lr_ratio: 0.05
    last_plateau_epochs: 4
  - !ExpWarmup
    epochs: 1

OptimizerBuilder:
  optimizer:
    type: Momentum
    momentum: 0.9
    use_nesterov: True
  regularizer:
    factor: 0.0005
    type: L2


TrainReader:
  batch_size: 6
  mosaic_epoch: 20

# detector configuration
architecture: YOLOX
pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/yolox_x_300e_coco.pdparams
norm_type: sync_bn
use_ema: True
ema_decay: 0.9999
ema_decay_type: "exponential"
act: silu
find_unused_parameters: True
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, 30] # 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: 1000
    keep_top_k: 100
    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.