yolov6_seg_s_300e_coco.yml

_BASE_: [
  '../datasets/coco_instance.yml',
  '../runtime.yml',
  '_base_/optimizer_300e.yml',
  '_base_/yolov6_seg_efficientrep.yml',
  '_base_/yolov6_seg_reader.yml',
]
depth_mult: 0.33
width_mult: 0.50
log_iter: 20
snapshot_epoch: 10
weights: output/yolov6_seg_s_300e_coco/model_final


### reader config
TrainReader:
  batch_size: 8 # default 8 gpus, total bs = 64

input_height: &input_height 640
input_width: &input_width 640
input_size: &input_size [*input_height, *input_width]

EvalReader:
  sample_transforms:
    - Decode: {}
    - Resize: {target_size: *input_size, keep_ratio: True, interp: 1}
    - Pad: {size: *input_size, fill_value: [114., 114., 114.]}
    - NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
    - Poly2Mask: {del_poly: True}
    - Permute: {}
  batch_size: 1

# EvalReader:
#   sample_transforms:
#     - Decode: {}
#     - Resize: {target_size: *input_size, keep_ratio: False, interp: 1}
#     #- Pad: {size: *input_size, fill_value: [114., 114., 114.]}
#     - NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
#     - Poly2Mask: {del_poly: True}
#     - Permute: {}
#   batch_size: 1

# # rect
# EvalReader:
#   sample_transforms:
#     - Decode: {}
#     - YOLOv5KeepRatioResize: {target_size: *input_size, batch_shapes: True, size_divisor: 32, extra_pad_ratio: 0.5}
#     - LetterResize: {scale: *input_size, pad_val: 144, allow_scale_up: False}
#     - NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
#     - Permute: {}
#   batch_size: 1 # only support bs=1


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

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

EffiDeInsHead:
  reg_max: 0
  use_dfl: False # False in n/s
  loss_weight: {cls: 1.0, iou: 2.5}