Karol BlaszczakandTatiana Savina
3394f654e7
--------- Co-authored-by: Tatiana Savina <tatiana.savina@intel.com>
166 lines
6.4 KiB
Markdown
166 lines
6.4 KiB
Markdown
# Proposal {#openvino_docs_ops_detection_Proposal_1}
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@sphinxdirective
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**Versioned name**: *Proposal-1*
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**Category**: *Object detection*
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**Short description**: *Proposal* operation filters bounding boxes and outputs only those with the highest prediction confidence.
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**Detailed description**
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*Proposal* has three inputs: a tensor with probabilities whether particular bounding box corresponds to background and foreground, a tensor with bbox_deltas for each of the bounding boxes, a tensor with input image size in the [``image_height``, ``image_width``, ``scale_height_and_width``] or [``image_height``, ``image_width``, ``scale_height``, ``scale_width``] format. The produced tensor has two dimensions ``[batch_size * post_nms_topn, 5]``, and for each output box contains batch index and box coordinates.
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*Proposal* layer does the following with the input tensor:
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1. Generates initial anchor boxes. Left top corner of all boxes is at (0, 0). Width and height of boxes are calculated from *base_size* with *scale* and *ratio* attributes.
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2. For each point in the first input tensor:
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* pins anchor boxes to the image according to the second input tensor that contains four deltas for each box: for *x* and *y* of center, for *width* and for *height*
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* finds out score in the first input tensor
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3. Filters out boxes with size less than *min_size*
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4. Sorts all proposals (*box*, *score*) by score from highest to lowest
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5. Takes top *pre_nms_topn* proposals
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6. Calculates intersections for boxes and filter out all boxes with :math:`intersection/union > nms\_thresh`
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7. Takes top *post_nms_topn* proposals
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8. Returns top proposals, if there is not enough proposals to fill the whole output tensor, the valid proposals will be terminated with a single -1.
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**Attributes**:
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* *base_size*
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* **Description**: *base_size* is the size of the anchor to which *scale* and *ratio* attributes are applied.
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* **Range of values**: a positive integer number
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* **Type**: ``int``
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* **Required**: *yes*
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* *pre_nms_topn*
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* **Description**: *pre_nms_topn* is the number of bounding boxes before the NMS operation. For example, *pre_nms_topn* equal to 15 means to take top 15 boxes with the highest scores.
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* **Range of values**: a positive integer number
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* **Type**: ``int``
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* **Required**: *yes*
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* *post_nms_topn*
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* **Description**: *post_nms_topn* is the number of bounding boxes after the NMS operation. For example, *post_nms_topn* equal to 15 means to take after NMS top 15 boxes with the highest scores.
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* **Range of values**: a positive integer number
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* **Type**: ``int``
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* **Required**: *yes*
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* *nms_thresh*
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* **Description**: *nms_thresh* is the minimum value of the proposal to be taken into consideration. For example, *nms_thresh* equal to 0.5 means that all boxes with prediction probability less than 0.5 are filtered out.
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* **Range of values**: a positive floating-point number
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* **Type**: ``float``
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* **Required**: *yes*
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* *feat_stride*
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* **Description**: *feat_stride* is the step size to slide over boxes (in pixels). For example, *feat_stride* equal to 16 means that all boxes are analyzed with the slide 16.
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* **Range of values**: a positive integer
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* **Type**: ``int``
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* **Required**: *yes*
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* *min_size*
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* **Description**: *min_size* is the minimum size of box to be taken into consideration. For example, *min_size* equal 35 means that all boxes with box size less than 35 are filtered out.
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* **Range of values**: a positive integer number
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* **Type**: ``int``
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* **Required**: *yes*
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* *ratio*
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* **Description**: *ratio* is the ratios for anchor generation.
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* **Range of values**: a list of floating-point numbers
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* **Type**: ``float[]``
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* **Required**: *yes*
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* *scale*
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* **Description**: *scale* is the scales for anchor generation.
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* **Range of values**: a list of floating-point numbers
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* **Type**: ``float[]``
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* **Required**: *yes*
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* *clip_before_nms*
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* **Description**: *clip_before_nms* flag that specifies whether to perform clip bounding boxes before non-maximum suppression or not.
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* **Range of values**: true or false
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* **Type**: ``boolean``
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* **Default value**: true
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* **Required**: *no*
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* *clip_after_nms*
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* **Description**: *clip_after_nms* is a flag that specifies whether to perform clip bounding boxes after non-maximum suppression or not.
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* **Range of values**: true or false
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* **Type**: ``boolean``
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* **Default value**: false
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* **Required**: *no*
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* *normalize*
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* **Description**: *normalize* is a flag that specifies whether to perform normalization of output boxes to *[0,1]* interval or not.
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* **Range of values**: true or false
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* **Type**: ``boolean``
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* **Default value**: false
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* **Required**: *no*
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* *box_size_scale*
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* **Description**: *box_size_scale* specifies the scale factor applied to bbox_deltas of box sizes before decoding.
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* **Range of values**: a positive floating-point number
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* **Type**: ``float``
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* **Default value**: 1.0
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* **Required**: *no*
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* *box_coordinate_scale*
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* **Description**: *box_coordinate_scale* specifies the scale factor applied to bbox_deltas of box coordinates before decoding.
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* **Range of values**: a positive floating-point number
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* **Type**: ``float``
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* **Default value**: 1.0
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* **Required**: *no*
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* *framework*
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* **Description**: *framework* specifies how the box coordinates are calculated.
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* **Range of values**:
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* "" (empty string) - calculate box coordinates like in Caffe
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* *tensorflow* - calculate box coordinates like in the TensorFlow* Object Detection API models
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* **Type**: string
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* **Default value**: "" (empty string)
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* **Required**: *no*
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**Inputs**:
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* **1**: 4D tensor of type *T* and shape ``[batch_size, 2*K, H, W]`` with class prediction scores. **Required.**
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* **2**: 4D tensor of type *T* and shape ``[batch_size, 4*K, H, W]`` with deltas for each bounding box. **Required.**
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* **3**: 1D tensor of type *T* with 3 or 4 elements: ``[image_height, image_width, scale_height_and_width]`` or ``[image_height, image_width, scale_height, scale_width]``. **Required.**
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**Outputs**:
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* **1**: Tensor of type *T* and shape ``[batch_size * post_nms_topn, 5]``.
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**Types**
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* *T*: floating-point type.
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**Example**
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.. code-block:: cpp
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<layer ... type="Proposal" ... >
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<data base_size="16" feat_stride="16" min_size="16" nms_thresh="0.6" post_nms_topn="200" pre_nms_topn="6000"
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ratio="2.67" scale="4.0,6.0,9.0,16.0,24.0,32.0"/>
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<input> ... </input>
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<output> ... </output>
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</layer>
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@endsphinxdirective
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