That's another one question about competition metric. I want to implement some metric MAP_metric(y_pred, y_true) and then compile my model this way:
\n\nmodel.compile(optimizer=..., loss=..., metrics=['MAP_metric])\nLet's say, I have N examples in my validation_set, and in M of that I have pictures without phneumonia and without bounding boxes. The problem is, that I need to summarize MAP_metric() for all examples of my validation set, and then devide this sum. Bat denominator have to be N - M instead of N, that I get by my implementation. Does anybody solve this problem? That's important, cause I want to get equivalence with validation and LB scores.
", "messages": [ { "id": "386616", "postDate": "09/13/2018 08:28:28", "content": "That's another one question about competition metric. I want to implement some metric MAP_metric(y_pred, y_true) and then compile my model this way:
\n\nmodel.compile(optimizer=..., loss=..., metrics=['MAP_metric])\nLet's say, I have N examples in my validation_set, and in M of that I have pictures without phneumonia and without bounding boxes. The problem is, that I need to summarize MAP_metric() for all examples of my validation set, and then devide this sum. Bat denominator have to be N - M instead of N, that I get by my implementation. Does anybody solve this problem? That's important, cause I want to get equivalence with validation and LB scores.
", "rawMarkdown": "That's another one question about competition metric. I want to implement some metric MAP_metric(y_pred, y_true) and then compile my model this way:\n\n model.compile(optimizer=..., loss=..., metrics=['MAP_metric])\n\nLet's say, I have N examples in my validation_set, and in M of that I have pictures without phneumonia and without bounding boxes. The problem is, that I need to summarize MAP_metric() for all examples of my validation set, and then devide this sum. Bat denominator have to be N - M instead of N, that I get by my implementation. Does anybody solve this problem? That's important, cause I want to get equivalence with validation and LB scores.", "votes": null }, { "id": "387327", "postDate": "09/14/2018 18:53:50", "content": "Have you tried what's been done here: https://www.kaggle.com/vbookshelf/keras-iou-metric-implemented-without-tensor-drama ?
", "rawMarkdown": "Have you tried what's been done here: https://www.kaggle.com/vbookshelf/keras-iou-metric-implemented-without-tensor-drama ?", "votes": null }, { "id": "388507", "postDate": "09/17/2018 05:37:50", "content": "Oh, no. I mean Mean Average Precision metric. Link that you give consist just IOU implementation.
", "rawMarkdown": "Oh, no. I mean Mean Average Precision metric. Link that you give consist just IOU implementation.", "votes": null }, { "id": "388887", "postDate": "09/17/2018 19:11:27", "content": "In this case - https://www.kaggle.com/chenyc15/mean-average-precision-metric hope it could help. I have adapted the same way and hope it is the official metric.
\n\nThere is this discussion https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/discussion/64860 where we would like to get the official implementation
", "rawMarkdown": "In this case - https://www.kaggle.com/chenyc15/mean-average-precision-metric hope it could help. I have adapted the same way and hope it is the official metric. \n\nThere is this discussion https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/discussion/64860 where we would like to get the official implementation", "votes": null } ], "comments": [ { "id": 387327, "author_name": "kretes", "author_url": "", "post_date": "09/14/2018 18:53:50", "content": "Have you tried what's been done here: https://www.kaggle.com/vbookshelf/keras-iou-metric-implemented-without-tensor-drama ?
", "votes": null, "replies": [ { "id": 388507, "author_name": "koza4ukdmitrij", "author_url": "", "post_date": "09/17/2018 05:37:50", "content": "Oh, no. I mean Mean Average Precision metric. Link that you give consist just IOU implementation.
", "votes": null, "replies": [] }, { "id": 388887, "author_name": "kretes", "author_url": "", "post_date": "09/17/2018 19:11:27", "content": "In this case - https://www.kaggle.com/chenyc15/mean-average-precision-metric hope it could help. I have adapted the same way and hope it is the official metric.
\n\nThere is this discussion https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/discussion/64860 where we would like to get the official implementation
", "votes": null, "replies": [] } ] } ], "raw_markdown_by_id": { "386616": "That's another one question about competition metric. I want to implement some metric MAP_metric(y_pred, y_true) and then compile my model this way:\n\n model.compile(optimizer=..., loss=..., metrics=['MAP_metric])\n\nLet's say, I have N examples in my validation_set, and in M of that I have pictures without phneumonia and without bounding boxes. The problem is, that I need to summarize MAP_metric() for all examples of my validation set, and then devide this sum. Bat denominator have to be N - M instead of N, that I get by my implementation. Does anybody solve this problem? That's important, cause I want to get equivalence with validation and LB scores.", "387327": "Have you tried what's been done here: https://www.kaggle.com/vbookshelf/keras-iou-metric-implemented-without-tensor-drama ?", "388507": "Oh, no. I mean Mean Average Precision metric. Link that you give consist just IOU implementation.", "388887": "In this case - https://www.kaggle.com/chenyc15/mean-average-precision-metric hope it could help. I have adapted the same way and hope it is the official metric. \n\nThere is this discussion https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/discussion/64860 where we would like to get the official implementation" }, "source": "meta" }