**Category**: *Object detection*

**Short description**: *Proposal* operation filters bounding boxes and outputs only those with the highest prediction confidence.

**Detailed description**

*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]`

. *Proposal* layer does the following with the input tensor:

- 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. - For each point in the first input tensor:
- 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* - finds out score in the first input tensor

- pins anchor boxes to the image according to the second input tensor that contains four deltas for each box: for
- Filters out boxes with size less than
*min_size* - Sorts all proposals (
*box*,*score*) by score from highest to lowest - Takes top
*pre_nms_topn*proposals - Calculates intersections for boxes and filter out all boxes with \(intersection/union > nms\_thresh\)
- Takes top
*post_nms_topn*proposals - Returns top proposals

*base_size***Description**:*base_size*is the size of the anchor to which*scale*and*ratio*attributes are applied.**Range of values**: a positive integer number**Type**:`int`

**Default value**: None**Required**:*yes*

*pre_nms_topn***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.**Range of values**: a positive integer number**Type**:`int`

**Default value**: None**Required**:*yes*

*post_nms_topn***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.**Range of values**: a positive integer number**Type**:`int`

**Default value**: None**Required**:*yes*

*nms_thresh***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.**Range of values**: a positive floating-point number**Type**:`float`

**Default value**: None**Required**:*yes*

*feat_stride***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.**Range of values**: a positive integer**Type**:`int`

**Default value**: None**Required**:*yes*

*min_size***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.**Range of values**: a positive integer number**Type**:`int`

**Default value**: None**Required**:*yes*

*ratio***Description**:*ratio*is the ratios for anchor generation.**Range of values**: a list of floating-point numbers**Type**:`float[]`

**Default value**: None**Required**:*yes*

*scale***Description**:*scale*is the scales for anchor generation.**Range of values**: a list of floating-point numbers**Type**:`float[]`

**Default value**: None**Required**:*yes*

*clip_before_nms***Description**:*clip_before_nms*flag that specifies whether to perform clip bounding boxes before non-maximum suppression or not.**Range of values**: True or False**Type**:`boolean`

**Default value**: True**Required**:*no*

*clip_after_nms***Description**:*clip_after_nms*is a flag that specifies whether to perform clip bounding boxes after non-maximum suppression or not.**Range of values**: True or False**Type**:`boolean`

**Default value**: False**Required**:*no*

*normalize***Description**:*normalize*is a flag that specifies whether to perform normalization of output boxes to*[0,1]*interval or not.**Range of values**: True or False**Type**:`boolean`

**Default value**: False**Required**:*no*

*box_size_scale***Description**:*box_size_scale*specifies the scale factor applied to bbox_deltas of box sizes before decoding.**Range of values**: a positive floating-point number**Type**:`float`

**Default value**: 1.0**Required**:*no*

*box_coordinate_scale***Description**:*box_coordinate_scale*specifies the scale factor applied to bbox_deltas of box coordinates before decoding.**Range of values**: a positive floating-point number**Type**:`float`

**Default value**: 1.0**Required**:*no*

*framework***Description**:*framework*specifies how the box coordinates are calculated.**Range of values**:- "" (empty string) - calculate box coordinates like in Caffe*
*tensorflow*- calculate box coordinates like in the TensorFlow* Object Detection API models

**Type**: string**Default value**: "" (empty string)**Required**:*no*

**Inputs**:

**1**: 4D input floating point tensor with class prediction scores. Required.**2**: 4D input floating point tensor with box bbox_deltas. Required.**3**: 1D input floating tensor 3 or 4 elements: [`image_height`

,`image_width`

,`scale_height_and_width`

] or [`image_height`

,`image_width`

,`scale_height`

,`scale_width`

]. Required.

**Outputs**:

**1**: Floating point tensor of shape`[batch_size * post_nms_topn, 5]`

.

**Example**

<layer ... type="Proposal" ... >

<data base_size="16" feat_stride="16" min_size="16" nms_thresh="0.6" post_nms_topn="200" pre_nms_topn="6000"

ratio="2.67" scale="4.0,6.0,9.0,16.0,24.0,32.0"/>

<input> ... </input>

<output> ... </output>

</layer>