首发:3D视觉工坊微信公众号
作者:Tom Hardy
@todo后期将会不断保持更新
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
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重点介绍:1、3D视觉算法;2、vslam算法;3、图像处理;4、深度学习;5、自动驾驶;6、技术干货。 博主及合伙人分别来国内自知名大厂、海康研究院,深研3D视觉、深度学习、图像处理、自动驾驶、目标检测、VSLAM算法等领域。
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