stem/AI/Neural Networks/CNN/FCN/FCN.md

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Fully [[Convolution]]al Network
[[Convolutional Layer|Convolutional]] and [[UpConv|up-convolutional layers]] with [[Activation Functions#ReLu|ReLu]] but no others (pooling)
- All some sort of Encoder-Decoder
Contractive → [[UpConv]]
# Image Segmentation
- For visual output
- Previously image $\rightarrow$ vector
- Additional layers to up-sample representation to an image
- Up-[[convolution]]al
- De-[[convolution]]al
![[fcn-uses.png]]
![[fcn-arch.png]]
# Training
- Rarely from scratch
- Pre-trained weights
- Replace final layers
- [[MLP|FC]] layers
- White-noise initialised
- Add [[upconv]] layer(s)
- Fine-tune train
- Freeze others
- Annotated GT images
- Can use summed per-pixel log [[Deep Learning#Loss Function|loss]]
# Evaluation
![[fcn-eval.png]]
- SDS
- Classical method
- 52% mAP
- FCN
- 62% mAP
- Intersection over Union
- IOU
- Jaccard
- Averaged over all images
- $J(A,B)=\frac{|A\cap B|}{|A\cup B|}$