{"action":"closed","issue":{"url":"https://api.github.com/repos/Bartzi/see/issues/5","repository_url":"https://api.github.com/repos/Bartzi/see","labels_url":"https://api.github.com/repos/Bartzi/see/issues/5/labels{/name}","comments_url":"https://api.github.com/repos/Bartzi/see/issues/5/comments","events_url":"https://api.github.com/repos/Bartzi/see/issues/5/events","html_url":"https://github.com/Bartzi/see/issues/5","id":297279307,"number":5,"title":"the choice of Chainer?","user":{"login":"AllAwake","id":4332614,"avatar_url":"https://avatars1.githubusercontent.com/u/4332614?v=4","gravatar_id":"","url":"https://api.github.com/users/AllAwake","html_url":"https://github.com/AllAwake","followers_url":"https://api.github.com/users/AllAwake/followers","following_url":"https://api.github.com/users/AllAwake/following{/other_user}","gists_url":"https://api.github.com/users/AllAwake/gists{/gist_id}","starred_url":"https://api.github.com/users/AllAwake/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/AllAwake/subscriptions","organizations_url":"https://api.github.com/users/AllAwake/orgs","repos_url":"https://api.github.com/users/AllAwake/repos","events_url":"https://api.github.com/users/AllAwake/events{/privacy}","received_events_url":"https://api.github.com/users/AllAwake/received_events","type":"User","site_admin":false},"labels":[],"state":"closed","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":4,"created_at":"2018-02-14T23:13:35Z","updated_at":"2018-02-15T04:03:33Z","closed_at":"2018-02-15T04:03:33Z","author_association":"NONE","body":"HI Christian,\r\ni've read your paper and it really makes sense - in fact i've been looking for a similar approach as i'm working on \"text recognition in the wild\" Currently i have a VGG-like arch in Keras/TF that works but i also have a rough PyTorch version too. \r\nMy quick question - was there a particular reason for you to go with Chainer vs say PyTorch? i don't know anything about Chainer but it sounds like they are somewhat similar - then i can just port your implementation to PyTorch right?\r\nAlso any assumptions about the size of input images? Don't see it's mentioned... I've experimented with both Yolo2 and RetinaNet and it's almost like you need to split the image before feeding into the pipeline (resizing is not good - as the text might be pretty small) - there are some implementation that have yet another  text/no text classifier...\r\nThanks much! helena"}}
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