{"action":"created","issue":{"url":"https://api.github.com/repos/zhengyang-wang/Deeplab-v2--ResNet-101--Tensorflow/issues/30","repository_url":"https://api.github.com/repos/zhengyang-wang/Deeplab-v2--ResNet-101--Tensorflow","labels_url":"https://api.github.com/repos/zhengyang-wang/Deeplab-v2--ResNet-101--Tensorflow/issues/30/labels{/name}","comments_url":"https://api.github.com/repos/zhengyang-wang/Deeplab-v2--ResNet-101--Tensorflow/issues/30/comments","events_url":"https://api.github.com/repos/zhengyang-wang/Deeplab-v2--ResNet-101--Tensorflow/issues/30/events","html_url":"https://github.com/zhengyang-wang/Deeplab-v2--ResNet-101--Tensorflow/issues/30","id":293067728,"number":30,"title":"Image order RGB or BRG?","user":{"login":"John1231983","id":24875971,"avatar_url":"https://avatars3.githubusercontent.com/u/24875971?v=4","gravatar_id":"","url":"https://api.github.com/users/John1231983","html_url":"https://github.com/John1231983","followers_url":"https://api.github.com/users/John1231983/followers","following_url":"https://api.github.com/users/John1231983/following{/other_user}","gists_url":"https://api.github.com/users/John1231983/gists{/gist_id}","starred_url":"https://api.github.com/users/John1231983/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/John1231983/subscriptions","organizations_url":"https://api.github.com/users/John1231983/orgs","repos_url":"https://api.github.com/users/John1231983/repos","events_url":"https://api.github.com/users/John1231983/events{/privacy}","received_events_url":"https://api.github.com/users/John1231983/received_events","type":"User","site_admin":false},"labels":[],"state":"closed","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":5,"created_at":"2018-01-31T08:17:26Z","updated_at":"2018-02-01T19:33:21Z","closed_at":"2018-02-01T17:51:30Z","author_association":"NONE","body":"Hello, I am using the official resnet-101 pre-trained model (as your link). It is trained from ImageNet, with image order is RGB and IMAGE_MEAN is \r\n```\r\n_R_MEAN = 123.68 / 255\r\n_G_MEAN = 116.78 / 255\r\n_B_MEAN = 103.94 / 255\r\n```\r\nThe official resnet-101 pre-processing [L223](https://github.com/tensorflow/models/blob/master/official/resnet/vgg_preprocessing.py#L223)  is\r\n\r\n```\r\nchannels = tf.split(axis=2, num_or_size_splits=num_channels, value=image)\r\n  for i in range(num_channels):\r\n    channels[i] -= means[i]\r\n  return tf.concat(axis=2, values=channels)\r\n```\r\nWhile your code is converting RGB to BRG and used another IMAGE_MEAN. I think we should you same pre-processing as pre-trained model did such as RGB order and imagenet image mean. Am I right?"},"comment":{"url":"https://api.github.com/repos/zhengyang-wang/Deeplab-v2--ResNet-101--Tensorflow/issues/comments/362376735","html_url":"https://github.com/zhengyang-wang/Deeplab-v2--ResNet-101--Tensorflow/issues/30#issuecomment-362376735","issue_url":"https://api.github.com/repos/zhengyang-wang/Deeplab-v2--ResNet-101--Tensorflow/issues/30","id":362376735,"user":{"login":"zhengyang-wang","id":7230680,"avatar_url":"https://avatars3.githubusercontent.com/u/7230680?v=4","gravatar_id":"","url":"https://api.github.com/users/zhengyang-wang","html_url":"https://github.com/zhengyang-wang","followers_url":"https://api.github.com/users/zhengyang-wang/followers","following_url":"https://api.github.com/users/zhengyang-wang/following{/other_user}","gists_url":"https://api.github.com/users/zhengyang-wang/gists{/gist_id}","starred_url":"https://api.github.com/users/zhengyang-wang/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/zhengyang-wang/subscriptions","organizations_url":"https://api.github.com/users/zhengyang-wang/orgs","repos_url":"https://api.github.com/users/zhengyang-wang/repos","events_url":"https://api.github.com/users/zhengyang-wang/events{/privacy}","received_events_url":"https://api.github.com/users/zhengyang-wang/received_events","type":"User","site_admin":false},"created_at":"2018-02-01T19:33:21Z","updated_at":"2018-02-01T19:33:21Z","author_association":"OWNER","body":"Yes."}}
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