# This config is an assembled config for ByteTrack MOT, used as eval/infer mode for MOT.
_BASE_: [
  '../../../yolov3/yolov3_darknet53_270e_coco.yml',
  '../_base_/mot17.yml',
]
weights: output/yolov3_darknet53_40e_608x608_mot17half/model_final
log_iter: 20
snapshot_epoch: 2

# schedule configuration for fine-tuning
epoch: 40
LearningRate:
  base_lr: 0.0001
  schedulers:
  - !PiecewiseDecay
    gamma: 0.1
    milestones:
    - 32
    - 36
  - !LinearWarmup
    start_factor: 0.3333333333333333
    steps: 100

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

TrainReader:
  batch_size: 8
  mixup_epoch: 35

# detector configuration
architecture: YOLOv3
pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/yolov3_darknet53_270e_coco.pdparams
norm_type: sync_bn

YOLOv3:
  backbone: DarkNet
  neck: YOLOv3FPN
  yolo_head: YOLOv3Head
  post_process: BBoxPostProcess

DarkNet:
  depth: 53
  return_idx: [2, 3, 4]

# use default config
# YOLOv3FPN:

YOLOv3Head:
  anchors: [[10, 13], [16, 30], [33, 23],
            [30, 61], [62, 45], [59, 119],
            [116, 90], [156, 198], [373, 326]]
  anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
  loss: YOLOv3Loss

YOLOv3Loss:
  ignore_thresh: 0.7
  downsample: [32, 16, 8]
  label_smooth: false

BBoxPostProcess:
  decode:
    name: YOLOBox
    conf_thresh: 0.005
    downsample_ratio: 32
    clip_bbox: true
  nms:
    name: MultiClassNMS
    keep_top_k: 100
    score_threshold: 0.01
    nms_threshold: 0.45
    nms_top_k: 1000