一、数据增强方式

  1. random erase
  2. CutOut
  3. MixUp
  4. CutMix
  5. 色彩、对比度增强
  6. 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

  1. VGG
  2. ResNet（ResNet18，50，100）
  3. ResNeXt
  4. DenseNet
  5. SqueezeNet
  6. Darknet（Darknet19,53）
  7. MobileNet
  8. ShuffleNet
  9. DetNet
  10. DetNAS
  11. SpineNet
  12. EfficientNet（EfficientNet-B0/B7）
  13. CSPResNeXt50
  14. CSPDarknet53

三、常用Head

Dense Prediction (one-stage):

  1. RPN
  2. SSD
  3. YOLO
  4. RetinaNet
  5. (anchor based)
  6. CornerNet
  7. CenterNet
  8. MatrixNet
  9. FCOS(anchor free)

Sparse Prediction (two-stage):

  1. Faster R-CNN
  2. R-FCN
  3. Mask RCNN (anchor based)
  4. RepPoints(anchor free)

四、常用neck

Additional blocks:

  1. SPP
  2. ASPP
  3. RFB
  4. SAM

Path-aggregation blocks:

  1. FPN
  2. PAN
  3. NAS-FPN
  4. Fully-connected FPN
  5. BiFPN
  6. ASFF
  7. SFAM
  8. NAS-FPN

五、Skip-connections

  1. Residual connections
  2. Weighted residual connections
  3. Multi-input weighted residual connections
  4. 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

 

摘自：https://bbs.cvmart.net/topics/2846?from=timeline