# ImageNet: what does top-five error means? [duplicate]

One of the evaluation method for ImageNet Competition (classify 1,000 categories images) is top-5 error, what does that mean?

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 $k=1, ..., n$.

• Great! One clarification, what does c and capitalized C mean? Jan 8, 2015 at 18:14
• Here big C is one of the ground truth labels and little c is one of the predicted labels. Jan 8, 2015 at 19:54
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]