{"action":"created","issue":{"url":"https://api.github.com/repos/matterport/Mask_RCNN/issues/777","repository_url":"https://api.github.com/repos/matterport/Mask_RCNN","labels_url":"https://api.github.com/repos/matterport/Mask_RCNN/issues/777/labels{/name}","comments_url":"https://api.github.com/repos/matterport/Mask_RCNN/issues/777/comments","events_url":"https://api.github.com/repos/matterport/Mask_RCNN/issues/777/events","html_url":"https://github.com/matterport/Mask_RCNN/issues/777","id":341262601,"node_id":"MDU6SXNzdWUzNDEyNjI2MDE=","number":777,"title":"Configuration parameters which control detection mask accuracy","user":{"login":"michaelmason","id":1066129,"node_id":"MDQ6VXNlcjEwNjYxMjk=","avatar_url":"https://avatars0.githubusercontent.com/u/1066129?v=4","gravatar_id":"","url":"https://api.github.com/users/michaelmason","html_url":"https://github.com/michaelmason","followers_url":"https://api.github.com/users/michaelmason/followers","following_url":"https://api.github.com/users/michaelmason/following{/other_user}","gists_url":"https://api.github.com/users/michaelmason/gists{/gist_id}","starred_url":"https://api.github.com/users/michaelmason/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/michaelmason/subscriptions","organizations_url":"https://api.github.com/users/michaelmason/orgs","repos_url":"https://api.github.com/users/michaelmason/repos","events_url":"https://api.github.com/users/michaelmason/events{/privacy}","received_events_url":"https://api.github.com/users/michaelmason/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":3,"created_at":"2018-07-14T19:48:14Z","updated_at":"2018-07-18T13:12:18Z","closed_at":null,"author_association":"NONE","body":"We’re using Mask RCNN for pixelwise cloud segmentation in whole-sky infrared camera images.  The camera is a ground-based infrared imaging system which points straight up towards the sky and captures images on a regular interval. \r\n\r\nCurrently we’re only using a single object class (cloud) and all other pixels are considered to be background.  We have training/validation/test ground-truth masks which classify each pixel as either cloud or not-cloud/background.  We’re using COCO pre-trained weights and we’re using the same approach that the Nucleus detection example uses (train the network heads for 20 epochs, then continue training all layers for N epochs). \r\n\r\nThe ground-truth masks are fairly detailed, in some cases very small groupings of pixels are classified as clouds.  This results in ground-truth clouds with edges which are not always well-defined as the clouds sometimes have thinner edges and they may taper off in some cases.\r\n\r\nWhen we evaluate the model, the detected cloud-masks are very, for lack of a better phrase, rounded off.  The cloud edges aren’t quite as intricate and detailed as the ground-truth masks. \r\n\r\nWith the overall goal of increasing the fine-detail and accuracy of the detected masks, here are my questions:\r\n\r\n(1) If we adjusted our training schedule, could we possibly get more accurate detection results?  \r\n\r\n(2) Are there any training or detection configuration parameters which may affect the accuracy of the detected masks?  Or, perhaps parts of the model code that could be adjusted to get better accuracy?\r\n\r\n(3) Any other advice or things to investigate which may help us?\r\n\r\nThank you all for your help and thanks to Matterport for this wonderful tool."},"comment":{"url":"https://api.github.com/repos/matterport/Mask_RCNN/issues/comments/405925237","html_url":"https://github.com/matterport/Mask_RCNN/issues/777#issuecomment-405925237","issue_url":"https://api.github.com/repos/matterport/Mask_RCNN/issues/777","id":405925237,"node_id":"MDEyOklzc3VlQ29tbWVudDQwNTkyNTIzNw==","user":{"login":"michaelmason","id":1066129,"node_id":"MDQ6VXNlcjEwNjYxMjk=","avatar_url":"https://avatars0.githubusercontent.com/u/1066129?v=4","gravatar_id":"","url":"https://api.github.com/users/michaelmason","html_url":"https://github.com/michaelmason","followers_url":"https://api.github.com/users/michaelmason/followers","following_url":"https://api.github.com/users/michaelmason/following{/other_user}","gists_url":"https://api.github.com/users/michaelmason/gists{/gist_id}","starred_url":"https://api.github.com/users/michaelmason/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/michaelmason/subscriptions","organizations_url":"https://api.github.com/users/michaelmason/orgs","repos_url":"https://api.github.com/users/michaelmason/repos","events_url":"https://api.github.com/users/michaelmason/events{/privacy}","received_events_url":"https://api.github.com/users/michaelmason/received_events","type":"User","site_admin":false},"created_at":"2018-07-18T13:12:17Z","updated_at":"2018-07-18T13:12:17Z","author_association":"NONE","body":"Hi Joel, thanks for responding.  No, we're not using mini masks.  I was under the impression that since we were very interested in getting as accurate detection masks as possible that downsampling the data using mini-masks would not help us.\r\n\r\nThanks, we'll review #635"}}
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