一、数据增强方式
random erase
CutOut
MixUp
CutMix
色彩、对比度增强
旋转、裁剪

解决数据不均衡：

Focal loss
hard negative example mining
OHEM
S-OHEM
GHM（较大关注easy和正常hard样本，较少关注outliners）
PISA

二、常用backbone
VGG
ResNet（ResNet18，50，100）
ResNeXt
DenseNet
SqueezeNet
Darknet（Darknet19,53）
MobileNet
ShuffleNet
DetNet
DetNAS
SpineNet
EfficientNet（EfficientNet-B0/B7）
CSPResNeXt50
CSPDarknet53

三、常用Head

Dense Prediction (one-stage):
RPN
SSD
YOLO
RetinaNet(anchor based)
CornerNet CenterNet
MatrixNet
FCOS(anchor free)
Sparse Prediction (two-stage):

Faster R-CNN
R-FCN
Mask RCNN (anchor based)
RepPoints(anchor free)

四、常用neck
Additional blocks:
SPP
ASPP
RFB
SAM
Path-aggregation blocks:
FPN
PAN
NAS-FPN
Fully-connected
FPN
BiFPN
ASFF
SFAM
NAS-FPN

五、Skip-connections

Residual connections
Weighted residual connections
Multi-input weighted residual connections
Cross stage partial connections (CSP)

六、常用激活函数和loss

激活函数：

ReLU 
LReLU 
PReLU
ReLU6
Scaled Exponential Linear Unit (SELU)
Swishhard-Swish
Mish

loss：
MSE
Smooth L1
Balanced L1
KL Loss
GHM loss
IoU Loss
Bounded IoU Loss
GIoU Loss
CIoU Loss
DIoU Loss

七、正则化和BN方式
正则化：

DropOut
DropPath
Spatial Drop
OutDropBlock

BN：

Batch Normalization (BN) 
Cross-GPU Batch Normalization (CGBN or SyncBN)
Filter Response Normalization (FRN)
Cross-Iteration Batch Normalization (CBN)

八、训练技巧

Label Smoothing
Warm Up