2020-08-10 13:24


  • 机器学习
  • 深度学习
  • 人工智能
  • 神经网络
  • 数据挖掘

《ImageNet Classification with Deep Convolutional
Neural Networks》
文章原文(位于section 6 Result):
“We also entered our model in the ILSVRC-2012 competition and report our results in Table 2. Since the ILSVRC-2012 test set labels are not publicly available,we cannot report test error rates for all the models that
we tried. In the remainder of this paragraph, we use validation and test error rates interchangeably because in our experience they do not differ by more than 0.1%(see Table 2). The CNN described in this paper achieves a top-5 error rate of 18.2%. Averaging the predictions of five similar CNNs gives an error rate of 16.4%. Training one CNN, with an extra sixth convolutional layer over the last pooling layer, to classify the entire ImageNet Fall 2011 release(15M images, 22K categories), and then “fine-tuning” it on ILSVRC-2012 gives an error rate of 16.6%.

Averaging the predictions of two CNNs that were pre-trained on the entire Fall 2011 release with the aforementioned five CNNs gives an error rate of 15.3%.

The second-best contest entry achieved an error rate of 26.2% with an approach that averages the predictions of several classifiers trained on FVs computed from different types of densely-sampled features [7].”

我就想问一下黑体加粗的那部分是指一张图片分别在7个CNN上面得到的softmax的各个类别的分数相加再除以7(2+5),得出最后这张图片的最终softmax的各类分数吗,然后考察top-5 error rate?


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