[GitHub] HuichuanLiu commented on issue #11149: Unreasonable performance of resnext models provided in model_zoo, evaluated by score.py

[GitBox <gi...@apache.org>]

HuichuanLiu commented on issue #11149: Unreasonable performance of resnext models provided in model_zoo, evaluated by score.py
URL: https://github.com/apache/incubator-mxnet/issues/11149#issuecomment-402089764
 
 
   OK, my colleague solves this problem.
   Just apply the Inception Preprocess, i.e. img/255-rgb_mean with --rgb-mean=[123.68,116.779,103.939]
   Then the imagenet1k-resnext-101-64x4d model delivers acc~=0.79, as good as your docs describe.
   
   I would strongly recommend you to modify or delete the description in the [mxnet imageclassifcation example page](https://github.com/apache/incubator-mxnet/tree/master/example/image-classification), it says
   
   > our Resnet does not need to specify the RGB mean due the data batch normalization layer. While the inception models needs --rgb-mean 123.68,116.779,103.939
   
   Although resnext is a hybrid variant of resnet and inception, it has a data batch normalization as first layers, so in my understanding, it does't need to reduce the rgb-mean.
   
   However, according to the experiment, it does indeed.
   

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