{"action":"created","issue":{"url":"https://api.github.com/repos/yulunzhang/RDN/issues/4","repository_url":"https://api.github.com/repos/yulunzhang/RDN","labels_url":"https://api.github.com/repos/yulunzhang/RDN/issues/4/labels{/name}","comments_url":"https://api.github.com/repos/yulunzhang/RDN/issues/4/comments","events_url":"https://api.github.com/repos/yulunzhang/RDN/issues/4/events","html_url":"https://github.com/yulunzhang/RDN/issues/4","id":328509479,"node_id":"MDU6SXNzdWUzMjg1MDk0Nzk=","number":4,"title":"About the validation result when I train just 1 epoch","user":{"login":"matsuneA212","id":18669901,"node_id":"MDQ6VXNlcjE4NjY5OTAx","avatar_url":"https://avatars3.githubusercontent.com/u/18669901?v=4","gravatar_id":"","url":"https://api.github.com/users/matsuneA212","html_url":"https://github.com/matsuneA212","followers_url":"https://api.github.com/users/matsuneA212/followers","following_url":"https://api.github.com/users/matsuneA212/following{/other_user}","gists_url":"https://api.github.com/users/matsuneA212/gists{/gist_id}","starred_url":"https://api.github.com/users/matsuneA212/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/matsuneA212/subscriptions","organizations_url":"https://api.github.com/users/matsuneA212/orgs","repos_url":"https://api.github.com/users/matsuneA212/repos","events_url":"https://api.github.com/users/matsuneA212/events{/privacy}","received_events_url":"https://api.github.com/users/matsuneA212/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":2,"created_at":"2018-06-01T12:55:56Z","updated_at":"2018-06-06T21:18:44Z","closed_at":null,"author_association":"NONE","body":"Hello. I have a question.\r\n\r\nI want to train the network by using SR291 dataset.\r\nI tried to change the parameters and code.\r\nHowever, when I train with 1 epoch, the validation result is too good (e.g. 37.0581).\r\nIt seems to be used some other model which I didn't train.\r\nSo, when I want to make new model with other dataset, where should I change?"},"comment":{"url":"https://api.github.com/repos/yulunzhang/RDN/issues/comments/395217111","html_url":"https://github.com/yulunzhang/RDN/issues/4#issuecomment-395217111","issue_url":"https://api.github.com/repos/yulunzhang/RDN/issues/4","id":395217111,"node_id":"MDEyOklzc3VlQ29tbWVudDM5NTIxNzExMQ==","user":{"login":"yulunzhang","id":22829440,"node_id":"MDQ6VXNlcjIyODI5NDQw","avatar_url":"https://avatars2.githubusercontent.com/u/22829440?v=4","gravatar_id":"","url":"https://api.github.com/users/yulunzhang","html_url":"https://github.com/yulunzhang","followers_url":"https://api.github.com/users/yulunzhang/followers","following_url":"https://api.github.com/users/yulunzhang/following{/other_user}","gists_url":"https://api.github.com/users/yulunzhang/gists{/gist_id}","starred_url":"https://api.github.com/users/yulunzhang/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/yulunzhang/subscriptions","organizations_url":"https://api.github.com/users/yulunzhang/orgs","repos_url":"https://api.github.com/users/yulunzhang/repos","events_url":"https://api.github.com/users/yulunzhang/events{/privacy}","received_events_url":"https://api.github.com/users/yulunzhang/received_events","type":"User","site_admin":false},"created_at":"2018-06-06T21:18:44Z","updated_at":"2018-06-06T21:18:44Z","author_association":"OWNER","body":"Hi,\r\n\r\nIt makes no difference. The validation set has no effect on the training process, no matter what validation images you use.\r\n\r\nBecause the image number and size are relatively small in SR291, RDN can be trained sufficiently on SR291 even within 1 or several epochs. In this early stage, there's no over-fitting problem.\r\n\r\nOnly when we continue to train RDN (with parameters stated in paper) on SR291 with much more epochs, will the model overfit SR291 and the performance also decrease obviously. \r\n\r\nIf we use parameters as  I stated above (For small dataset (e.g., SR291), you can decrease the input patch size (e.g., 24x24), halfLife (e.g.,2e4 iterations, namely 20 epochs), number of RDB (e.g., 10), total training epochs (e.g., 100). ), the overfitting effect can be alleviated."}}
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