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.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
and tiny_yolo_v2
.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
.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.face_person_detection
- converting face person detection model output with 2 detection outputs to ContainerPredition
, where value of parameters face_out
and person_out
are used for identification DetectionPrediction
in container.face_out
- face detection output layer name.person_out
- person detection output layer name.attributes_recognition
- converting vehicle attributes recognition model output to ContainerPrediction
where value of parameters color_out
and 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 DetectionPrediction
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.super_resolution
- converting output of single image super resolution network to SuperResolutionPrediction
.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
.