ImageNet: what does top-five error means? One of the evaluation method for ImageNet Competition (classify 1,000 categories images) is top-5 error, what does that mean?
See: http://www.image-net.org/challenges/LSVRC/
 A: Top-5 error, also known as rank-5 error is simply an instantiation of Rank-N error metric with $(N=5)$.
Rank $N$ error is the fraction of test samples $x_i$ where the correct label $y_i$ does not appear in the top $N$ predicted results of the model when results are sorted in decreasing order of confidence, or $P(y_i|x_i)$.
In ILSVRC 2014, the error metric for classification was:
\[
e = \tfrac{1}{n} \cdot \sum_k \min_{i} d(c_i, C_k)
\]
where
\[
  d(a, b) =
  \begin{cases}
   0       & \text{if } a = b \\
   1       & \text{otherwise}
  \end{cases}
\]
$c_i$ is the predicted label and $C_k$ is the ground truth label. There are $n$ possible labels with $k=1, ..., n$.
A: the top-5 error rate (of a model) is the fraction of test images for which the correct label is not among the five labels considered most probable by the model [1]
A: I think the conditional evaluation of $d(A_i, B_k)$ should read:
$d(Ai, Bk) = 0$, if $A_i$ occurs in the top N probable classifications for a
               given ground truth class Bk.
          = 1, if it does not.
Here k takes values 0 to N-1. Where N is the number of classes.
For each $B_k$, there are $A_i$ probable classifications, where i takes values 
[0,  N-1]
