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Maybe you should do a search and replace on these files.","created_at":"2017-06-28T13:42:42Z","updated_at":"2017-06-28T13:42:43Z","html_url":"https://github.com/azavea/raster-vision/pull/55#discussion_r124544081","pull_request_url":"https://api.github.com/repos/azavea/raster-vision/pulls/55","_links":{"self":{"href":"https://api.github.com/repos/azavea/raster-vision/pulls/comments/124544081"},"html":{"href":"https://github.com/azavea/raster-vision/pull/55#discussion_r124544081"},"pull_request":{"href":"https://api.github.com/repos/azavea/raster-vision/pulls/55"}}},"pull_request":{"url":"https://api.github.com/repos/azavea/raster-vision/pulls/55","id":127725399,"html_url":"https://github.com/azavea/raster-vision/pull/55","diff_url":"https://github.com/azavea/raster-vision/pull/55.diff","patch_url":"https://github.com/azavea/raster-vision/pull/55.patch","issue_url":"https://api.github.com/repos/azavea/raster-vision/issues/55","number":55,"state":"open","locked":false,"title":"Add DenseNet models and experiments","user":{"login":"citerana","id":16997590,"avatar_url":"https://avatars0.githubusercontent.com/u/16997590?v=3","gravatar_id":"","url":"https://api.github.com/users/citerana","html_url":"https://github.com/citerana","followers_url":"https://api.github.com/users/citerana/followers","following_url":"https://api.github.com/users/citerana/following{/other_user}","gists_url":"https://api.github.com/users/citerana/gists{/gist_id}","starred_url":"https://api.github.com/users/citerana/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/citerana/subscriptions","organizations_url":"https://api.github.com/users/citerana/orgs","repos_url":"https://api.github.com/users/citerana/repos","events_url":"https://api.github.com/users/citerana/events{/privacy}","received_events_url":"https://api.github.com/users/citerana/received_events","type":"User","site_admin":false},"body":"This PR adds the densenet121 and densenet169 models to the project. Both models support pretraining with imagenet weights. Additionally, this includes a series of experiments that were used to investigate the relative performances of the different architectures with variable learning rate schedules.\r\n\r\nThe link to the imagenet weights can be found at the top level comment of each model. These models use a tensorflow backend so choose the weights accordingly. 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