Refine the doc in detection_output API. #8689
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wangkuiyi
reviewed
Mar 1, 2018
| This layer applies the NMS to the output of network and computes the | ||
| predict bounding box location. The output's shape of this layer could | ||
| be zero if there is no valid bounding box. | ||
| This operation decode the predicted bboxes according to the prior bboxes |
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This operation updates bounding boxes by applying multi-class non-maximum suppression (NMS) and re-scoring.
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Re-write the doc. Thanks!
| @@ -91,7 +92,14 @@ class number, M is number of bounding boxes. For each category | |||
| nms_eta(float): The parameter for adaptive NMS. | |||
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| Returns: | |||
| The detected bounding boxes which are a Tensor. | |||
| Variable: The detection outputs which is a LoDTensor with shape [No, 6]. | |||
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There should be a comma (,) before "which".
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Re-write the doc. Thanks!
| @@ -91,7 +92,14 @@ class number, M is number of bounding boxes. For each category | |||
| nms_eta(float): The parameter for adaptive NMS. | |||
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| Returns: | |||
| The detected bounding boxes which are a Tensor. | |||
| Variable: The detection outputs which is a LoDTensor with shape [No, 6]. | |||
| Each row has 6 values: [label, confidence, xmin, ymin, xmax, ymax], | |||
| offset is N + 1, N is the batch size. If LoD[i + 1] - LoD[i] == 0, | ||
| means there is no detected bboxes for for i-th image. If there is | ||
| no detected boxes for all images, all the elements in LoD are 0, | ||
| and the Out only contains one value which is -1. |
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Re-write the doc. Thanks!
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Fix #8685