_BASE_: [
  '../datasets/coco_detection.yml',
  '../runtime.yml',
  '_base_/optimizer_300e.yml',
  '_base_/yolov6_efficientrep.yml',
  '_base_/yolov6_reader.yml',
]
depth_mult: 0.33
width_mult: 0.50
log_iter: 20
snapshot_epoch: 10
weights: output/yolov6_s_300e_coco/model_final


### reader config
TrainReader:
  batch_size: 32 # default 8 gpus, total bs = 256

EvalReader:
  batch_size: 1


### model config
act: 'relu'
training_mode: "repvgg"

YOLOv6:
  backbone: EfficientRep
  neck: RepBiFPAN
  yolo_head: EffiDeHead
  post_process: ~

EffiDeHead:
  reg_max: 0
  use_dfl: False # False in n/s
  loss_weight: {cls: 1.0, iou: 2.5}
  iou_type: 'giou'


### distill config
## Step 1: Training the base model, get about 44.3 mAP

## Step 2: Self-distillation training, get about 45.0 mAP

YOLOv6:
  backbone: EfficientRep
  neck: RepBiFPAN
  yolo_head: EffiDeHead_distill_ns
  post_process: ~

EffiDeHead_distill_ns:
  reg_max: 16
  use_dfl: True

## Please cancel the following comment and train again:
# self_distill: True
# pretrain_weights: output/yolov6_s_300e_coco/model_final.pdparams
# save_dir: output_distill
# weights: output_distill/yolov6_s_300e_coco/model_final
