Adapter is a function for conversion network infer output to metric specific format. You can use 2 ways to set adapter for topology:
type: for setting adapter name. This approach gives opportunity to set additional parameters for adapter if it is required.AccuracyChecker supports following set of adapters:
classification - converting output of classification model to ClassificationPrediction representation.segmentation - converting output of semantic segmentation model to SeegmentationPrediction representation.make_argmax - allows to apply argmax operation to output values.tiny_yolo_v1 - converting output of Tiny YOLO v1 model to DetectionPrediction representation.reid - converting output of reidentification model to ReIdentificationPrediction representation.grn_workaround - enabling processing output with adding Global Region Normalization layer.yolo_v2 - converting output of YOLO v2 family models to DetectionPrediction representation.classes - number of detection classes (default 20).anchors - anchor values provided as comma-separated list or one of precomputed:yolo_v2 - [1.3221, 1.73145, 3.19275, 4.00944, 5.05587, 8.09892, 9.47112, 4.84053, 11.2364, 10.0071],tiny_yolo_v2 - [1.08, 1.19, 3.42, 4.41, 6.63, 11.38, 9.42, 5.11, 16.62, 10.52]coords - number of bbox coordinates (default 4).num - num parameter from DarkNet configuration file (default 5).yolo_v3 - converting output of YOLO v3 family models to DetectionPrediction representation.classes - number of detection classes (default 80).anchors - anchor values provided as comma-separited list or precomputed:yolo_v3 - [10.0, 13.0, 16.0, 30.0, 33.0, 23.0, 30.0, 61.0, 62.0, 45.0, 59.0, 119.0, 116.0, 90.0, 156.0, 198.0, 373.0, 326.0]tiny_yolo_v3 - [10.0, 14.0, 23.0, 27.0, 37.0, 58.0, 81.0, 82.0, 135.0, 169.0, 344.0, 319.0]coords - number of bbox coordinates (default 4).num - num parameter from DarkNet configuration file (default 3).threshold - minimal objectness score value for valid detections (default 0.001).input_width and input_height - network input width and height correspondingly (default 416).outputs - the list of output layers names (optional), if specified there should be exactly 3 output layers provided.lpr - converting output of license plate recognition model to CharacterRecognitionPrediction representation.ssd - converting output of SSD model to DetectionPrediction representation.ssd_mxnet - converting output of SSD-based models from MxNet framework to DetectionPrediction representation.pytorch_ssd_decoder - converts output of SSD model from Pytorch without embedded decoder.scores_out - name of output layer with bounding boxes scores.boxes_out - name of output layer with bounding boxes coordinates.confidence_threshold - lower bound for valid boxes scores (optional, default 0.05).nms_threshold - overlap threshold for NMS (optional, default 0.5).keep_top_k - maximal number of boxes which should be kept (optional, default 200).tf_object_detection - converting output of detection models from TensorFlow object detection API to DetectionPrediction.classes_out - name of output layer with predicted classes.boxes_out - name of output layer with predicted boxes coordinates in format [y0, x0, y1, x1].scores_out - name of output layer with detection scores.num_detections_out - name of output layer which contains the number of valid detections.face_person_detection - converting face person detection model output with 2 detection outputs to ContainerPredition, where value of parameters face_outand person_out are used for identification DetectionPrediction in container.face_out - face detection output layer name.person_out - person detection output layer name.person_attributes - converting person attributes recognition model output to MultiLabelRecognitionPrediction.attributes_recognition_out - output layer name with attributes scores. (optional, used if your model has more than one outputs).vehicle_attributes - converting vehicle attributes recognition model output to ContainerPrediction where value of parameters color_outand type_out are used for identification ClassificationPrediction in container.color_out - vehicle color attribute output layer name.type_out- vehicle type attribute output layer name.head_pose - converting head pose estimation model output to ContainerPrediction where names of parameters angle_pitch, angle_yaw and angle_roll are used for identification RegressionPrediction in container.angle_pitch - output layer name for pitch angle.angle_yaw- output layer name for yaw angle.angle_roll - output layer name for roll angle.age_gender - converting age gender recognition model output to ContainerPrediction with ClassificationPrediction named gender for gender recognition, ClassificationPrediction named age_classification and RegressionPrediction named age_error for age recognition.age_out - output layer name for age recognition.gender_out - output layer name for gender recognition.action_detection - converting output of model for person detection and action recognition tasks to ContainerPrediction with DetectionPrdiction for class agnostic metric calculation and ActionDetectionPrediction for action recognition. The representations in container have names class_agnostic_prediction and action_prediction respectively.priorbox_out - name of layer containing prior boxes in SSD format.loc_out - name of layer containing box coordinates in SSD format.main_conf_out - name of layer containing detection confidences.add_conf_out_prefix - prefix for generation name of layers containing action confidences if topology has several following layers or layer name.add_conf_out_count - number of layers with action confidences (optional, you can not provide this argument if action confidences contained in one layer).num_action_classes - number classes for action recognition.detection_threshold - minimal detection confidences level for valid detections.actions_scores_threshold - minimal actions confidences level for valid detections.action_scale - scale for correct action score calculation.super_resolution - converting output of single image super resolution network to SuperResolutionPrediction.reverse_channels - allow switching output image channels e.g. RGB to BGR (Optional. Default value is False).landmarks_regression - converting output of model for landmarks regression to FacialLandmarksPrediction.text_detection - converting output of model for text detection to TextDetectionPrediction.pixel_class_out - name of layer containing information related to text/no-text classification for each pixel.pixel_link_out - name of layer containing information related to linkage between pixels and their neighbors.human_pose_estimation - converting output of model for human pose estimation to PoseEstimationPrediction.part_affinity_fields_out - name of output layer with keypoints pairwise relations (part affinity fields).keypoints_heatmap_out - name of output layer with keypoints heatmaps.beam_search_decoder - realization CTC Beam Search decoder for symbol sequence recognition, converting model output to CharacterRecognitionPrediction.beam_size - size of the beam to use during decoding (default 10).blank_label - index of the CTC blank label.softmaxed_probabilities - indicator that model uses softmax for output layer (default False).gaze_estimation - converting output of gaze estimation model to GazeVectorPrediction.hit_ratio_adapter - converting output NCF model to HitRatioPrediction.brain_tumor_segmentation - converting output of brain tumor segmentation model to BrainTumorSegmentationPrediction.