basecls.data.mixup#
Mixup and CutMix
Mixup: “Mixup: Beyond Empirical Risk Minimization”
CutMix: “CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features”
引用
rwightman/pytorch-image-models
- class basecls.data.mixup.MixupCutmixTransform(mixup_alpha=1.0, cutmix_alpha=0.0, cutmix_minmax=None, prob=1.0, switch_prob=0.5, mode='batch', data_format='HWC', num_classes=1000, calibrate_cutmix_lambda=True, calibrate_mixup_lambda=False, permute=False, *, order=None)[源代码]#
-
Implement Mixup and CutMix as VisionTransform.
备注
When composed in
Compose
,batch_compose
must be set toTrue
.- 参数
mixup_alpha (
float
) – mixup alpha value, mixup is active if > 0. Default:1.0
cutmix_alpha (
float
) – cutmix alpha value, cutmix is active if > 0. Default:0.0
cutmix_minmax (
Optional
[List
[float
]]) – cutmix min/max image ratio, cutmix is active and uses this vs alpha if not None. Default:None
prob (
float
) – probability of applying mixup or cutmix per batch or element. Default:1.0
switch_prob (
float
) – probability of switching to cutmix instead of mixup when both are active. Default: 0.5mode (
str
) – how to apply mixup/cutmix params, supports"batch"
,"pair"
(pair of elements) and"elem"
(element). Default:"batch"
data_format (
str
) –"CHW"
or"HWC"
, use"HWC"
if use this transform beforeT.ToMode()
. Default:"HWC"
num_classes (
int
) – number of classes for target. Default:1000
calibrate_cutmix_lambda (
bool
) – apply lambda correction when cutmix bbox clipped by image borders. Correction is based on clipped area for cutmix. Default:True
calibrate_mixup_lambda (
bool
) – enforce mixup lambda to be greater than 0.5, only make difference in"elem"
mode. Default:False
permute (
bool
) – whether mixup with permuted samples instead of flipped samples. Default:False