A six-model ensemble of residual networks of different depths reports 3.57% top-5 error on the ImageNet test set and won the ILSVRC 2015 classification task, improving on every comparison ensemble printed in Table 5.

A six-model ensemble of residual networks of different depths reports 3.57% top-5 error on the ImageNet test set and won the ILSVRC 2015 classification task, improving on every comparison ensemble printed in Table 5. · CiteArk

Measurement results

0/6 assessed

0 supported0 disputed or conflicting6 not assessed

The complete result group stays with its claim. Assessed rows come first; their judgments do not replace the whole-claim conclusion. Expand a row for conditions, sources, and evidence history.

Each row is a measurement with its reported value, latest available observation, and cumulative evidence state.
Reported	Latest observation	Evidence judgment
7.32%	—	Not assessed
6.66%	—	Not assessed
6.8%	—	Not assessed
4.94%	—	Not assessed
4.82%	—	Not assessed
3.57%	—	Not assessed

A six-model ensemble of residual networks of different depths reports 3.57% top-5 error on the ImageNet test set and won the ILSVRC 2015 classification task, improving on every comparison ensemble printed in Table 5.

Reported and observed measurements

Assessments (0)

Runs (0)

Measurement results