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)
- Swish
- hard-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 DropOut
- DropBlock
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