{"action":"opened","issue":{"url":"https://api.github.com/repos/matterport/Mask_RCNN/issues/1043","repository_url":"https://api.github.com/repos/matterport/Mask_RCNN","labels_url":"https://api.github.com/repos/matterport/Mask_RCNN/issues/1043/labels{/name}","comments_url":"https://api.github.com/repos/matterport/Mask_RCNN/issues/1043/comments","events_url":"https://api.github.com/repos/matterport/Mask_RCNN/issues/1043/events","html_url":"https://github.com/matterport/Mask_RCNN/issues/1043","id":372088909,"node_id":"MDU6SXNzdWUzNzIwODg5MDk=","number":1043,"title":"Changing backbone architecture","user":{"login":"JosephPB","id":25730310,"node_id":"MDQ6VXNlcjI1NzMwMzEw","avatar_url":"https://avatars1.githubusercontent.com/u/25730310?v=4","gravatar_id":"","url":"https://api.github.com/users/JosephPB","html_url":"https://github.com/JosephPB","followers_url":"https://api.github.com/users/JosephPB/followers","following_url":"https://api.github.com/users/JosephPB/following{/other_user}","gists_url":"https://api.github.com/users/JosephPB/gists{/gist_id}","starred_url":"https://api.github.com/users/JosephPB/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/JosephPB/subscriptions","organizations_url":"https://api.github.com/users/JosephPB/orgs","repos_url":"https://api.github.com/users/JosephPB/repos","events_url":"https://api.github.com/users/JosephPB/events{/privacy}","received_events_url":"https://api.github.com/users/JosephPB/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":0,"created_at":"2018-10-19T19:34:57Z","updated_at":"2018-10-19T19:34:57Z","closed_at":null,"author_association":"NONE","body":"I'd like to experiment with changing the backbone architecture to a model that has been trained on a dataset more similar to what I am segmenting, theoretically therefore producing more accurate RPN outputs.\r\n\r\nI see that in line 1896 of `model.py` the feature layers of the `resnet_graph` are being extracted and passed into the top-down layers which complete the RPN architecture. Am I right in thinking that I can just pass any feature maps I want to the top-down layers and then train the network as normal since during training the network only trains those top-down layers and above (i.e. segmentation and regression layers) and does not touch the underlying backbone layers, or do I have to provide the whole architecture of my alternative backbone and load in the pre-trained weights and then extract the feature maps as done in `model.py`?"}}
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