{"action":"opened","issue":{"url":"https://api.github.com/repos/fizyr/keras-retinanet/issues/626","repository_url":"https://api.github.com/repos/fizyr/keras-retinanet","labels_url":"https://api.github.com/repos/fizyr/keras-retinanet/issues/626/labels{/name}","comments_url":"https://api.github.com/repos/fizyr/keras-retinanet/issues/626/comments","events_url":"https://api.github.com/repos/fizyr/keras-retinanet/issues/626/events","html_url":"https://github.com/fizyr/keras-retinanet/issues/626","id":348425509,"node_id":"MDU6SXNzdWUzNDg0MjU1MDk=","number":626,"title":"signature of model.predict_on_batch(inputs) different from readme","user":{"login":"hendyadys","id":14146684,"node_id":"MDQ6VXNlcjE0MTQ2Njg0","avatar_url":"https://avatars3.githubusercontent.com/u/14146684?v=4","gravatar_id":"","url":"https://api.github.com/users/hendyadys","html_url":"https://github.com/hendyadys","followers_url":"https://api.github.com/users/hendyadys/followers","following_url":"https://api.github.com/users/hendyadys/following{/other_user}","gists_url":"https://api.github.com/users/hendyadys/gists{/gist_id}","starred_url":"https://api.github.com/users/hendyadys/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/hendyadys/subscriptions","organizations_url":"https://api.github.com/users/hendyadys/orgs","repos_url":"https://api.github.com/users/hendyadys/repos","events_url":"https://api.github.com/users/hendyadys/events{/privacy}","received_events_url":"https://api.github.com/users/hendyadys/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":0,"created_at":"2018-08-07T17:55:43Z","updated_at":"2018-08-07T17:55:43Z","closed_at":null,"author_association":"NONE","body":"i am trying to train keras-retinanet, but the outputs of model.predict_on_batch(inputs) seem different from the readme and https://github.com/fizyr/keras-retinanet/blob/master/examples/ResNet50RetinaNet.ipynb\r\n\r\nit is supposed to be \r\n`boxes, scores, labels = model.predict_on_batch(inputs)`\r\nbut seems to be \r\n`boxes, nms_classification  = model.predict_on_batch(inputs)`\r\n\r\nthis would be fine if nms_classification was correct, but using the old definitions that i dug up \r\n```\r\npredicted_labels = np.argmax(nms_classification[0, :, :], axis=1)\r\nscores = nms_classification[0, np.arange(nms_classification.shape[1]), predicted_labels]\r\nsig_indices = np.where(scores > 0.5)[0]\r\nprint(img_name, sig_indices, boxes.shape)\r\n```\r\ngives nonsense\r\n('000000008021.jpg', array([159242, 159245, 159251, 159254, 159394, 159395, 159397, 159398,\r\n       159403, 159404, 159407, 159542, 159547, 159548, 159551, 159557,\r\n       159560, 159704, 160671, 160674]), (1, 160956, 4),\r\n      )\r\nand box coordinates even have negative values.\r\n\r\nwhat am i missing?\r\ni am on Keras 2.2.0 and tensorflow 1.7.0 and up to date with keras-retinanet-master\r\n\r\ni tried training on coco and pascal, as well as my own dataset with csv\r\npython /keras-retinanet-master/keras_retinanet/bin/train.py --gpu 0 --backbone resnet50 --epochs 10 --batch-size 1 --snapshot-path ./snapshots/pascal pascal /path/to/pascal\r\n\r\npython /keras-retinanet-master/keras_retinanet/bin/train.py --gpu 1 --backbone resnet50 --epochs 10 --batch-size 1 --no-weights --snapshot-path /snapshots/my_data csv /path/to/mydata.csv /path/to/mydata_classes.csv"}}
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