194 lines
6.6 KiB
Markdown
194 lines
6.6 KiB
Markdown
## ExperimentalDetectronDetectionOutput <a name="ExperimentalDetectronDetectionOutput"></a> {#openvino_docs_ops_detection_ExperimentalDetectronDetectionOutput_6}
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**Versioned name**: *ExperimentalDetectronDetectionOutput-6*
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**Category**: Object detection
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**Short description**: The *ExperimentalDetectronDetectionOutput* operation performs non-maximum suppression to generate
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the detection output using information on location and score predictions.
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**Detailed description**: The operation performs the following steps:
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1. Applies deltas to boxes sizes [x<sub>1</sub>, y<sub>1</sub>, x<sub>2</sub>, y<sub>2</sub>] and takes coordinates of
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refined boxes according to the formulas:
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`x1_new = ctr_x + (dx - 0.5 * exp(min(d_log_w, max_delta_log_wh))) * box_w`
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`y0_new = ctr_y + (dy - 0.5 * exp(min(d_log_h, max_delta_log_wh))) * box_h`
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`x1_new = ctr_x + (dx + 0.5 * exp(min(d_log_w, max_delta_log_wh))) * box_w - 1.0`
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`y1_new = ctr_y + (dy + 0.5 * exp(min(d_log_h, max_delta_log_wh))) * box_h - 1.0`
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* `box_w` and `box_h` are width and height of box, respectively:
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`box_w = x1 - x0 + 1.0`
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`box_h = y1 - y0 + 1.0`
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* `ctr_x` and `ctr_y` are center location of a box:
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`ctr_x = x0 + 0.5f * box_w`
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`ctr_y = y0 + 0.5f * box_h`
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* `dx`, `dy`, `d_log_w` and `d_log_h` are deltas calculated according to the formulas below, and `deltas_tensor` is a
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second input:
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`dx = deltas_tensor[roi_idx, 4 * class_idx + 0] / deltas_weights[0]`
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`dy = deltas_tensor[roi_idx, 4 * class_idx + 1] / deltas_weights[1]`
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`d_log_w = deltas_tensor[roi_idx, 4 * class_idx + 2] / deltas_weights[2]`
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`d_log_h = deltas_tensor[roi_idx, 4 * class_idx + 3] / deltas_weights[3]`
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2. If *class_agnostic_box_regression* is `true` removes predictions for background classes.
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3. Clips boxes to the image.
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4. Applies *score_threshold* on detection scores.
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5. Applies non-maximum suppression class-wise with *nms_threshold* and returns *post_nms_count* or less detections per
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class.
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6. Returns *max_detections_per_image* detections if total number of detections is more than *max_detections_per_image*;
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otherwise, returns total number of detections and the output tensor is filled with undefined values for rest output
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tensor elements.
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**Attributes**:
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* *score_threshold*
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* **Description**: The *score_threshold* attribute specifies a threshold to consider only detections whose score are
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larger than the threshold.
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* **Range of values**: non-negative floating-point number
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* **Type**: float
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* **Default value**: None
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* **Required**: *yes*
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* *nms_threshold*
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* **Description**: The *nms_threshold* attribute specifies a threshold to be used in the NMS stage.
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* **Range of values**: non-negative floating-point number
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* **Type**: float
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* **Default value**: None
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* **Required**: *yes*
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* *num_classes*
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* **Description**: The *num_classes* attribute specifies the number of detected classes.
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* **Range of values**: non-negative integer number
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* **Type**: int
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* **Default value**: None
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* **Required**: *yes*
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* *post_nms_count*
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* **Description**: The *post_nms_count* attribute specifies the maximal number of detections per class.
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* **Range of values**: non-negative integer number
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* **Type**: int
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* **Default value**: None
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* **Required**: *yes*
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* *max_detections_per_image*
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* **Description**: The *max_detections_per_image* attribute specifies maximal number of detections per image.
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* **Range of values**: non-negative integer number
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* **Type**: int
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* **Default value**: None
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* **Required**: *yes*
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* *class_agnostic_box_regression*
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* **Description**: *class_agnostic_box_regression* attribute is a flag that specifies whether to delete background
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classes or not.
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* **Range of values**:
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* `true` means background classes should be deleted
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* `false` means background classes should not be deleted
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* **Type**: boolean
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* **Default value**: false
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* **Required**: *no*
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* *max_delta_log_wh*
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* **Description**: The *max_delta_log_wh* attribute specifies maximal delta of logarithms for width and height.
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* **Range of values**: floating-point number
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* **Type**: float
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* **Default value**: None
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* **Required**: *yes*
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* *deltas_weights*
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* **Description**: The *deltas_weights* attribute specifies weights for bounding boxes sizes deltas.
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* **Range of values**: a list of non-negative floating-point numbers
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* **Type**: float[]
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* **Default value**: None
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* **Required**: *yes*
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**Inputs**
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* **1**: A 2D tensor of type *T* with input ROIs, with shape `[number_of_ROIs, 4]` providing the ROIs as 4-tuples:
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[x<sub>1</sub>, y<sub>1</sub>, x<sub>2</sub>, y<sub>2</sub>]. The batch dimension of first, second, and third inputs
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should be the same. **Required.**
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* **2**: A 2D tensor of type *T* with shape `[number_of_ROIs, num_classes * 4]` providing deltas for input boxes.
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**Required.**
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* **3**: A 2D tensor of type *T* with shape `[number_of_ROIs, num_classes]` providing detections scores. **Required.**
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* **4**: A 2D tensor of type *T* with shape `[1, 3]` contains three elements
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`[image_height, image_width, scale_height_and_width]` providing input image size info. **Required.**
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**Outputs**
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* **1**: A 2D tensor of type *T* with shape `[max_detections_per_image, 4]` providing boxes indices.
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* **2**: A 1D tensor of type *T_IND* with shape `[max_detections_per_image]` providing classes indices.
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* **3**: A 1D tensor of type *T* with shape `[max_detections_per_image]` providing scores indices.
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**Types**
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* *T*: any supported floating-point type.
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* *T_IND*: `int64` or `int32`.
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**Example**
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```xml
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<layer ... type="ExperimentalDetectronDetectionOutput" version="opset6">
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<data class_agnostic_box_regression="false" deltas_weights="10.0,10.0,5.0,5.0" max_delta_log_wh="4.135166645050049" max_detections_per_image="100" nms_threshold="0.5" num_classes="81" post_nms_count="2000" score_threshold="0.05000000074505806"/>
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<input>
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<port id="0">
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<dim>1000</dim>
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<dim>4</dim>
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</port>
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<port id="1">
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<dim>1000</dim>
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<dim>324</dim>
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</port>
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<port id="2">
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<dim>1000</dim>
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<dim>81</dim>
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</port>
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<port id="3">
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<dim>1</dim>
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<dim>3</dim>
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</port>
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</input>
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<output>
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<port id="4" precision="FP32">
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<dim>100</dim>
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<dim>4</dim>
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</port>
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<port id="5" precision="I32">
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<dim>100</dim>
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</port>
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<port id="6" precision="FP32">
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<dim>100</dim>
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</port>
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<port id="7" precision="I32">
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<dim>100</dim>
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</port>
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</output>
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</layer>
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```
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