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/configs
/yolov5
/yolov5_s-p6-v62_syncbn_fast_8xb16-300e_coco.py
_base_ = 'yolov5_s-v61_syncbn_fast_8xb16-300e_coco.py' | |
# ========================modified parameters====================== | |
img_scale = (1280, 1280) # width, height | |
num_classes = 80 # Number of classes for classification | |
# Config of batch shapes. Only on val. | |
# It means not used if batch_shapes_cfg is None. | |
batch_shapes_cfg = dict( | |
img_size=img_scale[0], | |
# The image scale of padding should be divided by pad_size_divisor | |
size_divisor=64) | |
# Basic size of multi-scale prior box | |
anchors = [ | |
[(19, 27), (44, 40), (38, 94)], # P3/8 | |
[(96, 68), (86, 152), (180, 137)], # P4/16 | |
[(140, 301), (303, 264), (238, 542)], # P5/32 | |
[(436, 615), (739, 380), (925, 792)] # P6/64 | |
] | |
# Strides of multi-scale prior box | |
strides = [8, 16, 32, 64] | |
num_det_layers = 4 # The number of model output scales | |
loss_cls_weight = 0.5 | |
loss_bbox_weight = 0.05 | |
loss_obj_weight = 1.0 | |
# The obj loss weights of the three output layers | |
obj_level_weights = [4.0, 1.0, 0.25, 0.06] | |
affine_scale = 0.5 # YOLOv5RandomAffine scaling ratio | |
tta_img_scales = [(1280, 1280), (1024, 1024), (1536, 1536)] | |
# =======================Unmodified in most cases================== | |
model = dict( | |
backbone=dict(arch='P6', out_indices=(2, 3, 4, 5)), | |
neck=dict( | |
in_channels=[256, 512, 768, 1024], out_channels=[256, 512, 768, 1024]), | |
bbox_head=dict( | |
head_module=dict( | |
in_channels=[256, 512, 768, 1024], featmap_strides=strides), | |
prior_generator=dict(base_sizes=anchors, strides=strides), | |
# scaled based on number of detection layers | |
loss_cls=dict(loss_weight=loss_cls_weight * | |
(num_classes / 80 * 3 / num_det_layers)), | |
loss_bbox=dict(loss_weight=loss_bbox_weight * (3 / num_det_layers)), | |
loss_obj=dict(loss_weight=loss_obj_weight * | |
((img_scale[0] / 640)**2 * 3 / num_det_layers)), | |
obj_level_weights=obj_level_weights)) | |
pre_transform = _base_.pre_transform | |
albu_train_transforms = _base_.albu_train_transforms | |
train_pipeline = [ | |
*pre_transform, | |
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)), | |
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)) | |
test_pipeline = [ | |
dict(type='LoadImageFromFile', backend_args=_base_.backend_args), | |
dict(type='YOLOv5KeepRatioResize', scale=img_scale), | |
dict( | |
type='LetterResize', | |
scale=img_scale, | |
allow_scale_up=False, | |
pad_val=dict(img=114)), | |
dict(type='LoadAnnotations', with_bbox=True, _scope_='mmdet'), | |
dict( | |
type='mmdet.PackDetInputs', | |
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', | |
'scale_factor', 'pad_param')) | |
] | |
val_dataloader = dict( | |
dataset=dict(pipeline=test_pipeline, batch_shapes_cfg=batch_shapes_cfg)) | |
test_dataloader = val_dataloader | |
# Config for Test Time Augmentation. (TTA) | |
_multiscale_resize_transforms = [ | |
dict( | |
type='Compose', | |
transforms=[ | |
dict(type='YOLOv5KeepRatioResize', scale=s), | |
dict( | |
type='LetterResize', | |
scale=s, | |
allow_scale_up=False, | |
pad_val=dict(img=114)) | |
]) for s in tta_img_scales | |
] | |
tta_pipeline = [ | |
dict(type='LoadImageFromFile', backend_args=_base_.backend_args), | |
dict( | |
type='TestTimeAug', | |
transforms=[ | |
_multiscale_resize_transforms, | |
[ | |
dict(type='mmdet.RandomFlip', prob=1.), | |
dict(type='mmdet.RandomFlip', prob=0.) | |
], [dict(type='mmdet.LoadAnnotations', with_bbox=True)], | |
[ | |
dict( | |
type='mmdet.PackDetInputs', | |
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', | |
'scale_factor', 'pad_param', 'flip', | |
'flip_direction')) | |
] | |
]) | |
] | |