payload: {"action":"created","issue":{"url":"https://api.github.com/repos/lxtGH/GALD-Net/issues/1","repository_url":"https://api.github.com/repos/lxtGH/GALD-Net","labels_url":"https://api.github.com/repos/lxtGH/GALD-Net/issues/1/labels{/name}","comments_url":"https://api.github.com/repos/lxtGH/GALD-Net/issues/1/comments","events_url":"https://api.github.com/repos/lxtGH/GALD-Net/issues/1/events","html_url":"https://github.com/lxtGH/GALD-Net/issues/1","id":424373598,"node_id":"MDU6SXNzdWU0MjQzNzM1OTg=","number":1,"title":"the availability of the code","user":{"login":"smajida","id":12903362,"node_id":"MDQ6VXNlcjEyOTAzMzYy","avatar_url":"https://avatars3.githubusercontent.com/u/12903362?v=4","gravatar_id":"","url":"https://api.github.com/users/smajida","html_url":"https://github.com/smajida","followers_url":"https://api.github.com/users/smajida/followers","following_url":"https://api.github.com/users/smajida/following{/other_user}","gists_url":"https://api.github.com/users/smajida/gists{/gist_id}","starred_url":"https://api.github.com/users/smajida/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/smajida/subscriptions","organizations_url":"https://api.github.com/users/smajida/orgs","repos_url":"https://api.github.com/users/smajida/repos","events_url":"https://api.github.com/users/smajida/events{/privacy}","received_events_url":"https://api.github.com/users/smajida/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":3,"created_at":"2019-03-22T19:47:31Z","updated_at":"2019-06-14T09:09:48Z","closed_at":null,"author_association":"NONE","body":"Hi, \r\n\r\nI am creating a new benchmark with fine-grained annotation in aerial imagery. In the paper, I am doing a heavy benchmark on state-of-the-art methods such as PSPNet, Deeplabv+, BiSeNet, DenseASSP and so on for semantic segmentation. I am wondering whether it is possible to train your algorithm on this data for the sake of benchmark with proper citation.\r\n\r\n"},"comment":{"url":"https://api.github.com/repos/lxtGH/GALD-Net/issues/comments/502033436","html_url":"https://github.com/lxtGH/GALD-Net/issues/1#issuecomment-502033436","issue_url":"https://api.github.com/repos/lxtGH/GALD-Net/issues/1","id":502033436,"node_id":"MDEyOklzc3VlQ29tbWVudDUwMjAzMzQzNg==","user":{"login":"smajida","id":12903362,"node_id":"MDQ6VXNlcjEyOTAzMzYy","avatar_url":"https://avatars3.githubusercontent.com/u/12903362?v=4","gravatar_id":"","url":"https://api.github.com/users/smajida","html_url":"https://github.com/smajida","followers_url":"https://api.github.com/users/smajida/followers","following_url":"https://api.github.com/users/smajida/following{/other_user}","gists_url":"https://api.github.com/users/smajida/gists{/gist_id}","starred_url":"https://api.github.com/users/smajida/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/smajida/subscriptions","organizations_url":"https://api.github.com/users/smajida/orgs","repos_url":"https://api.github.com/users/smajida/repos","events_url":"https://api.github.com/users/smajida/events{/privacy}","received_events_url":"https://api.github.com/users/smajida/received_events","type":"User","site_admin":false},"created_at":"2019-06-14T09:09:48Z","updated_at":"2019-06-14T09:09:48Z","author_association":"NONE","body":"> Hi ! @smajida I also created a benchmark for cityscape dataset. Now, I have re-implemented nearly all the state-of-art semantic segmentation model on cityscape dataset(PSPnet, deeplabb3+, deeplabv3, DAnet, DenseASPP, ). All of the models and codes including our GALD-net will be released in this repo.\r\n\r\nHi @lxtGH thanks for your kind reply. I am waiting for the release of the code. We have received reviews for our paper (unfortunately without GALD-Net) and working on the rebuttal, but we could add it for the next version. I am also adding \"Searching for Efficient Multi-Scale Architectures for Dense Image Prediction\" which got 82.7% on CityScapes on our dataset which is called \"SkyScapes\" :)"}}
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