# DetectionOutput {#openvino_docs_ops_detection_DetectionOutput_1} **Versioned name**: *DetectionOutput-1* **Category**: *Object detection* **Short description**: *DetectionOutput* performs non-maximum suppression to generate the detection output using information on location and confidence predictions. **Detailed description**: [Reference](https://arxiv.org/pdf/1512.02325.pdf). The layer has 3 mandatory inputs: tensor with box logits, tensor with confidence predictions and tensor with box coordinates (proposals). It can have 2 additional inputs with additional confidence predictions and box coordinates described in the [article](https://arxiv.org/pdf/1711.06897.pdf). The output tensor contains information about filtered detections described with 7 element tuples: `[batch_id, class_id, confidence, x_1, y_1, x_2, y_2]`. The first tuple with `batch_id` equal to `-1` means end of output. At each feature map cell, *DetectionOutput* predicts the offsets relative to the default box shapes in the cell, as well as the per-class scores that indicate the presence of a class instance in each of those boxes. Specifically, for each box out of k at a given location, *DetectionOutput* computes class scores and the four offsets relative to the original default box shape. This results in a total of \f$(c + 4)k\f$ filters that are applied around each location in the feature map, yielding \f$(c + 4)kmn\f$ outputs for a *m \* n* feature map. **Attributes**: * *num_classes* * **Description**: number of classes to be predicted * **Range of values**: positive integer number * **Type**: int * **Required**: *yes* * *background_label_id* * **Description**: background label id. If there is no background class, set it to -1. * **Range of values**: integer values * **Type**: int * **Default value**: 0 * **Required**: *no* * *top_k* * **Description**: maximum number of results to be kept per batch after NMS step. -1 means keeping all bounding boxes. * **Range of values**: integer values * **Type**: int * **Default value**: -1 * **Required**: *no* * *variance_encoded_in_target* * **Description**: *variance_encoded_in_target* is a flag that denotes if variance is encoded in target. If flag is false then it is necessary to adjust the predicted offset accordingly. * **Range of values**: false or true * **Type**: boolean * **Default value**: false * **Required**: *no* * *keep_top_k* * **Description**: maximum number of bounding boxes per batch to be kept after NMS step. -1 means keeping all bounding boxes after NMS step. * **Range of values**: integer values * **Type**: int[] * **Required**: *yes* * *code_type* * **Description**: type of coding method for bounding boxes * **Range of values**: "caffe.PriorBoxParameter.CENTER_SIZE", "caffe.PriorBoxParameter.CORNER" * **Type**: string * **Default value**: "caffe.PriorBoxParameter.CORNER" * **Required**: *no* * *share_location* * **Description**: *share_location* is a flag that denotes if bounding boxes are shared among different classes. * **Range of values**: false or true * **Type**: boolean * **Default value**: true * **Required**: *no* * *nms_threshold* * **Description**: threshold to be used in the NMS stage * **Range of values**: floating-point values * **Type**: float * **Required**: *yes* * *confidence_threshold* * **Description**: only consider detections whose confidences are larger than a threshold. If not provided, consider all boxes. * **Range of values**: floating-point values * **Type**: float * **Default value**: 0 * **Required**: *no* * *clip_after_nms* * **Description**: *clip_after_nms* flag that denotes whether to perform clip bounding boxes after non-maximum suppression or not. * **Range of values**: false or true * **Type**: boolean * **Default value**: false * **Required**: *no* * *clip_before_nms* * **Description**: *clip_before_nms* flag that denotes whether to perform clip bounding boxes before non-maximum suppression or not. * **Range of values**: false or true * **Type**: boolean * **Default value**: false * **Required**: *no* * *decrease_label_id* * **Description**: *decrease_label_id* flag that denotes how to perform NMS. * **Range of values**: * false - perform NMS like in Caffe. * true - perform NMS like in Apache MxNet. * **Type**: boolean * **Default value**: false * **Required**: *no* * *normalized* * **Description**: *normalized* flag that denotes whether input tensor with proposal boxes is normalized. If tensor is not normalized then *input_height* and *input_width* attributes are used to normalize box coordinates. * **Range of values**: false or true * **Type**: boolean * **Default value**: false * **Required**: *no* * *input_height (input_width)* * **Description**: input image height (width). If the *normalized* is 1 then these attributes are not used. * **Range of values**: positive integer number * **Type**: int * **Default value**: 1 * **Required**: *no* * *objectness_score* * **Description**: threshold to sort out confidence predictions. Used only when the *DetectionOutput* layer has 5 inputs. * **Range of values**: non-negative float number * **Type**: float * **Default value**: 0 * **Required**: *no* **Inputs** * **1**: 2D input tensor with box logits with shape `[N, num_prior_boxes * num_loc_classes * 4]` and type *T*. `num_loc_classes` is equal to `num_classes` when `share_location` is 0 or it's equal to 1 otherwise. **Required.** * **2**: 2D input tensor with class predictions with shape `[N, num_prior_boxes * num_classes]` and type *T*. **Required.** * **3**: 3D input tensor with proposals with shape `[priors_batch_size, 1, num_prior_boxes * prior_box_size]` or `[priors_batch_size, 2, num_prior_boxes * prior_box_size]`. `priors_batch_size` is either 1 or `N`. Size of the second dimension depends on `variance_encoded_in_target`. If `variance_encoded_in_target` is equal to 0, the second dimension equals to 2 and variance values are provided for each boxes coordinates. If `variance_encoded_in_target` is equal to 1, the second dimension equals to 1 and this tensor contains proposals boxes only. `prior_box_size` is equal to 4 when `normalized` is set to 1 or it's equal to 5 otherwise. **Required.** * **4**: 2D input tensor with additional class predictions information described in the [article](https://arxiv.org/pdf/1711.06897.pdf). Its shape must be equal to `[N, num_prior_boxes * 2]`. **Optional.** * **5**: 2D input tensor with additional box predictions information described in the [article](https://arxiv.org/pdf/1711.06897.pdf). Its shape must be equal to first input tensor shape. **Optional.** **Outputs** * **1**: 4D output tensor with type *T*. Its shape depends on `keep_top_k` or `top_k` being set. It `keep_top_k[0]` is greater than zero, then the shape is `[1, 1, N * keep_top_k[0], 7]`. If `keep_top_k[0]` is set to -1 and `top_k` is greater than zero, then the shape is `[1, 1, N * top_k * num_classes, 7]`. Otherwise, the output shape is equal to `[1, 1, N * num_classes * num_prior_boxes, 7]`. **Types** * *T*: any supported floating-point type. **Example** ```xml 1 5376 1 2688 1 2 5376 1 1 200 7 ```