What does the notation mAP@[.5:.95] mean?
For detection, a common way to determine if one object proposal was right is Intersection over Union (IoU, IU). This takes the set $A$ of proposed object pixels and the set of true object pixels $B$ and calculates: $$IoU(A, B) = \frac{A \cap B}{A \cup B}$$ Commonly, IoU > 0.5 means that it was a hit, otherwise it was a fail. For each class, one can calculate the True Positive ($TP(c)$): a proposal
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What are deconvolutional layers?
I recently read Fully Convolutional Networks for Semantic Segmentation by Jonathan Long, Evan Shelhamer, Trevor Darrell. I don't understand what "deconvolutional layers" do / how they work. The relevant part is 3.3. Upsampling is backwards strided convolution Another way to connect coarse outputs to dense pixels is interpolation. For instance, simple bilinear interpolation computes each output $y_
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