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_base_ = './yolov5_s-p6-v62_syncbn_fast_8xb16-300e_coco.py'
# ========================modified parameters======================
deepen_factor = 0.67
widen_factor = 0.75
lr_factor = 0.1
affine_scale = 0.9
loss_cls_weight = 0.3
loss_obj_weight = 0.7
mixup_prob = 0.1
# =======================Unmodified in most cases==================
num_classes = _base_.num_classes
num_det_layers = _base_.num_det_layers
img_scale = _base_.img_scale
model = dict(
backbone=dict(
deepen_factor=deepen_factor,
widen_factor=widen_factor,
),
neck=dict(
deepen_factor=deepen_factor,
widen_factor=widen_factor,
),
bbox_head=dict(
head_module=dict(widen_factor=widen_factor),
loss_cls=dict(loss_weight=loss_cls_weight *
(num_classes / 80 * 3 / num_det_layers)),
loss_obj=dict(loss_weight=loss_obj_weight *
((img_scale[0] / 640)**2 * 3 / num_det_layers))))
pre_transform = _base_.pre_transform
albu_train_transforms = _base_.albu_train_transforms
mosaic_affine_pipeline = [
dict(
type='Mosaic',
img_scale=img_scale,
pad_val=114.0,
pre_transform=pre_transform),
dict(
type='YOLOv5RandomAffine',
max_rotate_degree=0.0,
max_shear_degree=0.0,
scaling_ratio_range=(1 - affine_scale, 1 + affine_scale),
# img_scale is (width, height)
border=(-img_scale[0] // 2, -img_scale[1] // 2),
border_val=(114, 114, 114))
]
# enable mixup
train_pipeline = [
*pre_transform, *mosaic_affine_pipeline,
dict(
type='YOLOv5MixUp',
prob=mixup_prob,
pre_transform=[*pre_transform, *mosaic_affine_pipeline]),
dict(
type='mmdet.Albu',
transforms=albu_train_transforms,
bbox_params=dict(
type='BboxParams',
format='pascal_voc',
label_fields=['gt_bboxes_labels', 'gt_ignore_flags']),
keymap={
'img': 'image',
'gt_bboxes': 'bboxes'
}),
dict(type='YOLOv5HSVRandomAug'),
dict(type='mmdet.RandomFlip', prob=0.5),
dict(
type='mmdet.PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip',
'flip_direction'))
]
train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
default_hooks = dict(param_scheduler=dict(lr_factor=lr_factor))
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