class DbKeys:
    """
    This is for database ground truth and model runner output usage. It contains both key and value info, including examples.
    the value_example will follow the role of value_type, some of them are "list" but looks like a "string" since there are undeclare names.
    Please contact jeff@kneron.us for further info and modifications.
    """

    MONGO_OBJ_ID = {"key": '_id'}

    # TODO: In future, we may merge image path into image info. But based on current structure, we separated them.
    # image path attribute.
    IMAGE_PATH = {"key": "img_path",
                  "value_type": str,
                  "value_example": "/a/b/c/d.jpg",  # this is string.
                  "value_empty": " null (for json and db)\
                                   None (for python output) "}

    # image info attribute.
    IMAGE_INFO = {"key": "img_info",
                  "value_type": dict,
                  # The db value under "img_info" key is dictionary which store image info.
                  "value_example": {
                      "width": 640,
                      "height": 480,
                      "format": "jpg",
                      "blur_score": 0.5,
                  },
                  "value_empty": "null or [] (for json and db)\
                                  None or [] (for python output) "}
    # image size.(For INFERENCE database)
    IMAGE_SIZE = {"key": "img_size",
                  "value_type": list,
                  "value_example": "[640,480]", # [w,h]
                  "value_empty": " null or []  (for json and db)\
                                   None or [] (for python output) "}
                                   
    # detection bounding box attribute.
    BBOX = {"key": "bbox",
            "value_type": list,
            # This is 2D list, we use string here due to undeclare names. This is for multi face usage.
            # "class1, class2 is mapped to detection_map, default is kneron_detection"
            "value_example": " [[x1, y1, w1, h1, score1, class1], [x2, y2, w2, h2, score2, class2], ... ] ",
            "value_empty": " null or [] (for json and db)\
                             None or [] (for python output) "}
                             
    # For mutipleFDR.
    BBOX_EMB = {"key": "bbox_emb",
            "value_type": list,
            # This is 3D list for multi face FDR usage.
            #   -  bbox is a 1D list  [x,y,w,h,score, class]
            #   -  emb is a 1D list. 
            #                    |--------face1----------|   |--------face2----------|
            "value_example": " [ [[x1, y1, w1, h1, score1, class1],[emb1]],  [[x2, y2, w2, h2, score2, class2],[emb2]] ... ] ",
            "value_empty": " null or [] (for json and db)\
                             None or [] (for python output) "}
                             
    # detection OCR attribute.
    LP_OCR = {"key": "lp_ocr",
            "value_type": list,
            # This is list of string, showing OCR result
            "value_example": ["abcd1234", "efgh5678"],
            "value_empty": " null or [] (for json and db)\
                             None or [] (for python output) "}

     # 4 point car lincese plate landmark attribute.
    LP_LMK_4PTS = {"key": "lp_lmk_4pts",
                   "value_type": list,
                   # This is a 2D list, list of license plate 4 coordinate landmark list. Now model only outputs i license plate each inputs.
                   # the length of the inner list should be 9, the last two item is score, class:
                   # [[2*4 x,y coordinates, score ]]
                   "value_example": " [ [ licensepalte_topleft_x1, licensepalte_topleft_y1, \
                                          licensepalte_topright_x1, licensepalte_topright_y1, \
                                          licensepalte_bottomright_x1, licensepalte_bottomright_y1, \
                                          licensepalte_bottomleft_x1, licensepalte_bottomleft_y1 \
                                          ] ]",
                   "value_empty": " null or [] (for json and db) None or [] (for python return) "}

    # 5 point face landmark attribute.
    LMK_5PTS = {"key": "lmk_5pts",
                "value_type": list,
                # This is a 2D list, list of landmark list. First dimension is the face count, we use string here due to undeclare names.
                # the length of the inner list should be 11, the last item is 2D list contains score and classID:
                # [[2*5 x,y coordinates, score], [...], [...]]
                "value_example": " [ [ actual_right_eye_center_x1, actual_right_eye_center_y1, \
                                     actual_left_eye_center_x1, actual_left_eye_center_y1, \
                                     nose_x1, nose_y1, \
                                     actual_right_mouth_x1, actual_right_mouth_y1, \
                                     actual_left_mouth_x1, actual_left_mouth_y1],[second_person's landmark], [third_person's landmark]]",
                "value_empty": " null or [] (for json and db)\
                                 None or [] (for python return) "}

    # 2 point eye pupil landmark attribute.
    LMK_EYE_7PTS = {"key": "lmk_eye_7pts",
                    "value_type": list,
                    # This is a 2D list, list of list with two eye pupil landmarks.
                    "value_example": " [ [ \
                                            right_eye_60_x1, right_eye_60_y1, \
                                            right_eye_61_x1, right_eye_61_y1, \
                                            right_eye_63_x1, right_eye_63_y1, \
                                            right_eye_64_x1, right_eye_64_y1, \
                                            right_eye_65_x1, right_eye_65_y1, \
                                            right_eye_67_x1, right_eye_67_y1, \
                                            right_eye_96_x1, right_eye_96_y1, \
                                            left_eye_68_x1, left_eye_68_y1, \
                                            left_eye_69_x1, left_eye_69_y1, \
                                            left_eye_71_x1, left_eye_71_y1, \
                                            left_eye_72_x1, left_eye_72_y1, \
                                            left_eye_73_x1, left_eye_73_y1, \
                                            left_eye_75_x1, left_eye_75_y1, \
                                            left_eye_97_x1, left_eye_97_y1, \
                                           ], \
                                            [second_person's eye 7 pts], [third_person's eye 7 pts]]",
                    "value_empty": " null or [] (for json and db)\
                                    None or [] (for python return) "}

    # 11 point upper body landmark attribute.
    LMK_UPPER_BODY_7PTS = {"key": "lmk_upper_body_7pts",
                    "value_type": list,
                    # This is a 2D list, list of list with 11 upper body points' landmarks.
                    "value_example": " [ [  nose_x1, nose_y1, \
                                            actual_right_shoulder_x1, actual_right_shoulder_y1, \
                                            actual_right_elbow_x1, actual_right_elbow_y1, \
                                            actual_right_wrist_x1, actual_right_wrist_y1, \
                                            actual_left_shoulder_x1, actual_left_shoulder_y1, \
                                            actual_left_elbow_x1, actual_left_elbow_y1, \
                                            actual_left_wrist_x1, actual_left_wrist_y1, \
                                           ], \
                                            [second_person's 7 pts], [third_person's 7 pts]]",
                    "value_empty": " null or [] (for json and db)\
                                    None or [] (for python return) "}

    # 11 point upper body landmark attribute.
    LMK_UPPER_BODY_11PTS = {"key": "lmk_upper_body_11pts",
                    "value_type": list,
                    # This is a 2D list, list of list with 11 upper body points' landmarks.
                    "value_example": " [ [  nose_x1, nose_y1, \
                                            actual_right_eye_center_x1, actual_right_eye_center_y1, \
                                            actual_left_eye_center_x1, actual_left_eye_center_y1, \
                                            actual_right_ear_x1, actual_right_ear_y1, \
                                            actual_left_ear_x1, actual_left_ear_y1, \
                                            actual_right_shoulder_x1, actual_right_shoulder_y1, \
                                            actual_left_shoulder_x1, actual_left_shoulder_y1, \
                                            actual_right_elbow_x1, actual_right_elbow_y1, \
                                            actual_left_elbow_x1, actual_left_elbow_y1, \
                                            actual_right_wrist_x1, actual_right_wrist_y1, \
                                            actual_left_wrist_x1, actual_left_wrist_y1, \
                                           ], \
                                            [second_person's 11 pts], [third_person's 11 pts]]",
                    "value_empty": " null or [] (for json and db)\
                                    None or [] (for python return) "}
    
    # 17 point upper body landmark attribute.
    LMK_COCO_BODY_17TS = {"key": "lmk_coco_body_17pts",
                    "value_type": list,
                    # This is a 2D list, list of list with 17 upper body points' landmarks. In order of x, y, score
                    "value_example": " [ [  nose,            # 1 \
                                            actual_right_eye,        # 2 \
                                            actual_left_eye,       # 3 \
                                            actual_right_ear,        # 4 \
                                            actual_left_ear,       # 5 \
                                            actual_right_shoulder,   # 6 \
                                            actual_left_shoulder,  # 7 \
                                            actual_right_elbow,      # 8 \
                                            actual_left_elbow,     # 9 \
                                            actual_right_wrist,      # 10 \
                                            actual_left_wrist,     # 11 \
                                            actual_right_hip,        # 12 \
                                            actual_left_hip,       # 13 \
                                            actual_right_knee,       # 14 \
                                            actual_left_knee,      # 15 \
                                            actual_right_ankle,      # 16 \
                                            actual_left_ankle,     # 17 \
                                           ], \
                                            [second_person's 17 pts], [third_person's 17 pts]]",
                    "value_empty": " null or [] (for json and db)\
                                    None or [] (for python return) "}
                                    
    # Image label class, indicate what this image is.
    CLASS1 = {"key": "class1",
             "value_type": list,
             # This is 1D  of List of score(float) and class_id(int).
             # e.g. Refer detection_map["kneron_detection"] bwlow: class id 15 is "person". 22 is "mask", 24 is "sunglass"
             "value_example": "[score, class_1_id]",
             "value_empty": " null or [] (for json and db)\
                              None or [] (for python return) "}

     # Image label classes, indicate what this image is.
    CLASS2 = {"key": "class2",
             "value_type": list,
             # This is 2D  of List of score(float) and class_id(int).
             # The second dimension is for multiple people in one image
             # e.g. Refer detection_map["kneron_detection"] bwlow: class id 15 is "person". 22 is "mask", 24 is "sunglass"
             "value_example": "[[score, class_1_id], [score, class_2_id], [score, class_3_id]]",
             "value_empty": " null or [] (for json and db)\
                              None or [] (for python return) "}

     # Image label classes to support multiple classes per image, indicate what this image is.
    CLASS3 = {"key": "class3",
             "value_type": list,
             # This is 3D  of List of score(float) and class_id(int).
             # The second dimension is for multiple people in one image
             # The third dimension is for multiple class on a person
             # e.g. Refer detection_map["kneron_detection"] bwlow: class id 15 is "person". 22 is "mask", 24 is "sunglass"
             "value_example": "[[[score, class_1_id], [score, class_2_id], [score, class_3_id]]]",
             "value_empty": " null or [] (for json and db)\
                              None or [] (for python return) "}

    # embedding attribute support multiple faces in one image, for face recognition or similar regression model.
    EMBEDDING = {"key": "emb",
                 "value_type": list,
                 # This is 2D list of list of float, list size is embedding size, which could be 256 or 512 or any length.
                 # If an image includes multiple people, there would be multiple embedding in the list.
                 "value_example": "[[embedding 1], [embedding2]]",
                 "value_empty": " null or [] (for json and db)\
                                  None or [] (for python return) "}
    # age attribute.
    AGE = {"key": "age",
                  "value_type": list,
                  # This is 2D list of int and string for multi face usage. the following example is the result from a image of two persons.
                  "value_example": [[16]],
                  "value_empty": " null or [] (for json and db)\
                                   None or [] (for python return) "}

    # megaface test attribute.
    MEGAFACE_IMG_TYPE = {"key": "megaface_img_type",
                         "value_type": str,
                         # this is string, use "probe" or "distractor" for mega face test usage.
                         "value_example": "probe or distractor",
                         "value_empty": " null or [] (for json and db)\
                                          None or [] (for python return) "}
                                          
    # megaface test attribute.
    MULTIPLEFDR_IMG_TYPE = {"key": "multipleFDR_img_type",
                            "value_type": str,
                            # this is string, use "probe" or "distractor" for multiple person test usage.
                            "value_example": "probe or distractor",
                            "value_empty": " null or [] (for json and db)\
                                            None or [] (for python return) "}
                                            
    # liveness attribute.
    LIVENESS = {"key": "liveness",
                "value_type": list,
                # This is 1D list of int and float. we use string here due to undeclare names.
                # return attack or real. e.g. ["attack", 0.238] or ["real", 0.998]
                "value_example": "['real', score] or ['attack', score]",
                "value_empty": " null or [] (for json and db)\
                                 None or [] (for python return) "}

    # channel type of images.
    CHANNEL_TYPE = {"key": "channel_type",
                    "value_type": str,
                    "value_example": "NIR or RGB",
                    "value_empty": " null or [] (for json and db)\
                                     None or [] (for python return) "}
                                     
    # channel type format of images.(For INFERENCE database)
    CHANNEL_FORMAT= {"key": "channel_format",
                    "value_type": str,
                    "value_example": "NIR888 or RGB565",
                    "value_empty": " null or [] (for json and db)\
                                     None or [] (for python return) "}

    # for face recognition or similar tests' paring usage in pair collections.
    SAME_PAIRS = {"key": "same_pairs",
                  "value_type": list,
                  # This is 2D list of strings.
                  "value_example": [['/a/b/c/d.jpg, /a/b/c/e.jpg'], ['/a/b/c/f.jpg, /a/b/c/g.jpg']],
                  "value_empty": " null or [] (for json and db)\
                                   None or [] (for python return) "}

    # for face recognition or similar tests' paring usage in pair collections.
    DIFF_PAIRS = {"key": "diff_pairs",
                  "value_type": list,
                  # This is 2D list of strings.
                  "value_example": [['/a/b/c/d.jpg, /a/b/c/e.jpg'], ['/a/b/c/f.jpg, /a/b/c/g.jpg']],
                  "value_empty": " null or [] (for json and db)\
                                   None or [] (for python return) "}

    # for imagenet test usage.
    P1D_P5D = {"key": "p1d_p5d",
               "value_type": list,
               "value_example": " [ p1d, p5d ] ",
               "value_empty": " null or [] (for json and db)\
                                None or [] (for python return) "}

    # for storing numpy images:
    NUMPY_IMAGES = {"key": "numpy_image",
                    "value_type": list,
                    "value_example": "[[255,255,255..],[0,255,0...]]",
                    "value_empty": " null (for json and db)\
                                     None (for python output) "}

    NUMPY_SEGS = {"key": "numpy_segs",
                  "value_type": list,
                  "value_example": "[[0.1324,0.555, ...],[0.344,0.143,...]]",
                  "value_empty": " null (for json and db)\
                                   None (for python output) "}

    LANDMARK_SCORE = {"key": "landmark_score",
                      "value_type": list,
                      "value_example": "[[0.96010160446167, 0.950203835964203, 0.914104223251343, 0.644165992736816, 0.326085150241852]]",
                      "value_empty": " null or [] (for json and db)\
                                       None or [] (for python return) "}

    # tracking reid
    TRACK_ID = {"key": "track_id",
                "value_type": list,
                "value_example": "[[x1, y1, w1, h1, person_id], [x1, y1, w1, h1, person_id]]", #person id for multiple images, depending on skip_rate
                "value_empty": " null or [] (for json and db)\
                                 None or [] (for python return) "}

    PITCH_YAW_ROLL = {"key": "pitch_yaw_roll",
                     "value_type": list,
                     # A inside list means a person's yaw, pitch, and row angle
                     "value_example": '[[first_person_pitch_angle, first_person_yaw_angle, first_person_roll_angle], \
                                       [second_person_pitch_angle, second_person_pitch_angle, second_person_pitch_angle]]',
                     "value_empty": " null or [] (for json and db)\
                                      None or [] (for python return) "}

    # megaface test attribute.
    FEATURE_POINT_IMG_TYPE = {"key": "feature_point_img_type",
                         "value_type": str,
                         # this is string, use "probe" or "distractor" for mega face test usage.
                         "value_example": "src or dst",
                         "value_empty": " null or [] (for json and db)\
                                          None or [] (for python return) "}

    # image path attribute.
    SOURCE_PATH = {"key": "source_path",
                  "value_type": str,
                  "value_example": "/a/b/c/d.jpg",  # this is string.
                  "value_empty": " null (for json and db)\
                                   None (for python output) "}

    # image path attribute.
    T_MATRIX = {"key": "t_matrix",
                  "value_type": str,
                  "value_example": "/a/b/c/d.txt",  # this is a matrix.
                  "value_empty": " null (for json and db)\
                                   None (for python output) "}

    #feature point matching
    FEATURE_POINT = {"key": "feature_point",
                  "value_type": list,
                  # in predictions, the value example is like the following format
                  #landmark is a list of list of 2 floats, ex: [[10.2, 12.2], [11.4, 15.4], ... ]
                  #score is a list of float [ 0.3, 0.6, ...]
                  #descriptor is a list of embedding [[embedding], [embedding], ... ]
                  "value_example": "[list of points], [list of scores], [descriptor]]",  # this is a list.
                  "value_empty": " null (for json and db)\
                                   None (for python output) "}

    # instance segmentation attribute for prediction.
    INS_SEG = {"key": "ins_seg",
               "value_type": list,
                # This is 2D list, showing every instance information in the image
                # rle is a dictionary of the mask encoding, including ‘size’: [h, w], ‘counts’: encoding string. 
                # score is the confidence of mask. class is the category label of the mask in COCO
                "value_example": " [[rle1, score1, class1], [rle2, score2, class2], ... ] ",
                "value_empty": " null or [] (for json and db)\
                                 None or [] (for python output)"}
                                 
    # instance segmentation attribute for GT ONLY.
    SEGMENTATION = {"key": "segmentation",
               "value_type": list,
                # This is 3D list, showing every instance information in the image
                # class is the category label of the mask in COCO
                "value_example": " [[[seg_list1], [seg_list2], class1], [[seg_list3], [seg_list4], [seg_list5], class2], ... ] ",
                "value_empty": " null or [] (for json and db)\
                                 None or [] (for python output)"}
    
    # semantic segmentation attribute for prediction.
    SEMANTIC = {"key": "semantic",
               "value_type": list,
                # This is 3D list, showing every pixle-wise information in a image
                # It's a list including W x H elements
                "value_example": " [[length of height of image], [length of height of image], ... ] ",
                "value_empty": " null or [] (for json and db)\
                                 None or [] (for python output)"}

    # semantic segmentation attribute for prediction.
    PAIR_TYPE = {"key": "pair_type",
               "value_type": str,
                # This is 3D list, showing every pixle-wise information in a image
                # It's a list including W x H elements
                "value_example": "same",
                "value_empty": " null or [] (for json and db)\
                                 None or [] (for python output)"}


"""This is a detection map, for detection class mapping usage. Default is using "kneron_detection".
   Detection model should return corresponding int keys. Ex. If detected as "sunglass", return 24.
   If model need to follow other mapping style, please contact jeff@kneron.us to added another mapping in this dictionary.
"""
detection_map = {
    "kneron_detection": {
        0: "background",
        1: "aeroplane",
        2: "bicycle",
        3: "bird",
        4: "boat",
        5: "bottle",
        6: "bus",
        7: "car",
        8: "cat",
        9: "chair",
        10: "cow",
        11: "diningtable",
        12: "dog",
        13: "horse",
        14: "motorbike",
        15: "person",
        16: "pottedplant",
        17: "sheep",
        18: "sofa",
        19: "train",
        20: "tvmonitor",
        21: "face",
        22: "mask",
        23: "glass",
        24: "sunglass",
        25: "truck",
        26: "rider",
        27: "vehicle",
        28: "carlicenseplatelandmark",
        29: "no_chin",
        30: "no_nose",
        31: "no_eye",
        32: "no_face",
        33: "no_head",
        34: "filtered",
        35: "no_filtered",
        36: "female",
        37: "male",
        38: "anger",
        39: "disgust",
        40: "fear",
        41: "happy",
        42: "sad",
        43: "surprised",
        44: "normal",
        45: "two_wheel",
        46: "plate",
        47: "actual_right_eye_open",
        48: "actual_leftt_eye_open", 
        49: "actual_right_eye_close", 
        50: "actual_left_eye_close",
        51: "white_person",
        52: "black_person",
        53: "asian_person",
        54: "indian_person",
        55: "others_person",
        56: "head"
    },

    # This is for facial recognition
    "landmark_detection": {
        0: "0",
        1: "1",
        2: "2",
        3: "3",
        4: "4",
        5: "5",
        6: "6",
        7: "7",
        8: "8",
        9: "9",
        10: "10",
        11: "11",
        12: "12",
        13: "13",
        14: "14",
        15: "15",
        16: "16",
        17: "17",
        18: "18",
        19: "19",
        20: "20",
        21: "21",
        22: "22",
        23: "23",
        24: "24",
        25: "25",
        26: "26",
        27: "27",
        28: "28",
        29: "29",
        30: "30",
        31: "31",
        32: "32",
        33: "33",
        34: "34",
        35: "35",
        36: "36",
        37: "37",
        38: "38",
        39: "39",
        40: "40",
        41: "41",
        42: "42",
        43: "43",
        44: "44",
        45: "45",
        46: "46",
        47: "47",
        48: "48",
        49: "49",
        50: "50",
        51: "51",
        52: "52",
        53: "53",
        54: "54",
        55: "55",
        56: "56",
        57: "57",
        58: "58",
        59: "59",
        60: "60",
        61: "61",
        62: "62",
        63: "63",
        64: "64",
        65: "65",
        66: "66",
        67: "67",
        68: "68",
        69: "69",
        70: "70",
        71: "71",
        72: "72",
        73: "73",
        74: "74",
        75: "75",
        76: "76",
        77: "77",
        78: "78",
        79: "79",
        80: "80",
        81: "81",
        82: "82",
        83: "83",
        84: "84",
        85: "85",
        86: "86",
        87: "87",
        88: "88",
        89: "89",
        90: "90",
        91: "91",
        92: "92",
        93: "93",
        94: "94",
        95: "95",
        96: "96",
        97: "97",
    },

    "yolo_voc_detection": {
        1: "aeroplane",
        2: "bicycle",
        3: "bird",
        4: "boat",
        5: "bottle",
        6: "bus",
        7: "car",
        8: "cat",
        9: "chair",
        10: "cow",
        11: "diningtable",
        12: "dog",
        13: "horse",
        14: "motorbike",
        15: "person",
        16: "pottedplant",
        17: "sheep",
        18: "sofa",
        19: "train",
        20: "tvmonitor",
    },

    "coco": {
        1: 'person',
        2: 'bicycle',
        3: 'car',
        4: 'motorcycle',
        5: 'airplane',
        6: 'bus',
        7: 'train',
        8: 'truck',
        9: 'boat',
        10: 'traffic light',
        11: 'fire hydrant',
        12: 'stop sign',
        13: 'parking meter',
        14: 'bench',
        15: 'bird',
        16: 'cat',
        17: 'dog',
        18: 'horse',
        19: 'sheep',
        20: 'cow',
        21: 'elephant',
        22: 'bear',
        23: 'zebra',
        24: 'giraffe',
        25: 'backpack',
        26: 'umbrella',
        27: 'handbag',
        28: 'tie',
        29: 'suitcase',
        30: 'frisbee',
        31: 'skis',
        32: 'snowboard',
        33: 'sports ball',
        34: 'kite',
        35: 'baseball bat',
        36: 'baseball glove',
        37: 'skateboard',
        38: 'surfboard',
        39: 'tennis racket',
        40: 'bottle',
        41: 'wine glass',
        42: 'cup',
        43: 'fork',
        44: 'knife',
        45: 'spoon',
        46: 'bowl',
        47: 'banana',
        48: 'apple',
        49: 'sandwich',
        50: 'orange',
        51: 'broccoli',
        52: 'carrot',
        53: 'hot dog',
        54: 'pizza',
        55: 'donut',
        56: 'cake',
        57: 'chair',
        58: 'couch',
        59: 'potted plant',
        60: 'bed',
        61: 'dining table',
        62: 'toilet',
        63: 'tv',
        64: 'laptop',
        65: 'mouse',
        66: 'remote',
        67: 'keyboard',
        68: 'cell phone',
        69: 'microwave',
        70: 'oven',
        71: 'toaster',
        72: 'sink',
        73: 'refrigerator',
        74: 'book',
        75: 'clock',
        76: 'vase',
        77: 'scissors',
        78: 'teddy bear',
        79: 'hair drier',
        80: 'toothbrush'
    },

    "ocr": {
        1: "0", 
        2: "1", 
        3: "2", 
        4: "3", 
        5: "4", 
        6: "5", 
        7: "6", 
        8: "7", 
        9: "8", 
        10: "9", 
        11: "A", 
        12: "B", 
        13: "C", 
        14: "D", 
        15: "E", 
        16: "F", 
        17: "G", 
        18: "H", 
        19: "I", 
        20: "J", 
        21: "K", 
        22: "L", 
        23: "M", 
        24: "N", 
        25: "P", 
        26: "Q", 
        27: "R", 
        28: "S", 
        29: "T", 
        30: "U", 
        31: "V", 
        32: "W", 
        33: "X", 
        34: "Y", 
        35: "Z", 
        36: "plate",
    },

    "multiple_FDR":{
        0: "unknown",
        1: "Charlie",
        2: "Rob",
        3: "Danny",
        4: "Glenn",
        5: "Kaitlyn",
        6: "Mary"
    },

    "semantic_segmentation_41classes" : {
        0: 'wall',
        1: 'floor', 
        2: 'cabinet', 
        3: 'bed', 
        4: 'chair', 
        5: 'sofa',
        6: 'table', 
        7: 'door', 
        8: 'window', 
        9: 'bookshelf', 
        10: 'picture',
        11: 'counter', 
        12: 'blinds', 
        13: 'desk', 
        14: 'shelves', 
        15: 'curtain',
        16: 'dresser', 
        17: 'pillow', 
        18: 'mirror', 
        19: 'floor mat', 
        20: 'clothes',
        21: 'ceiling', 
        22: 'books', 
        23: 'refridgerator', 
        24: 'television',
        25: 'paper', 
        26: 'towel', 
        27: 'shower curtain', 
        28: 'box', 
        29: 'whiteboard',
        30: 'person', 
        31: 'night stand', 
        32: 'toilet', 
        33: 'sink', 
        34: 'lamp',
        35: 'bathtub', 
        36: 'bag',
        37: 'otherstructure', 
        38: 'otherfurniture', 
        39: 'otherprop',
        255:'void'
    },

    "kws" : {
        0: "negative",
        1: "heysnips"
    },

    "simple_command" : {
        0: "_unknown_",
        1: "yes",
        2: "no",
        3: "up",
        4: "down",
        5: "left",
        6: "right",
        7: "on",
        8: "off",
        9: "stop",
        10: "go"
    },

    "sound_classification" : {
        0: "air conditioner",
        1: "car horn",
        2: "children playing",
        3: "dog bark",
        4: "drilling",
        5: "engine idling",
        6: "gun shot",
        7: "jackhammer",
        8: "siren",
        9: "street music",
        10: "speech",
        11: "silence",
        12: "negative"
    },

    "imagenet_classification" : {
        0: "tench",
        1: "goldfish",
        2: "great white shark",
        3: "tiger shark",
        4: "hammerhead",
        5: "electric ray",
        6: "stingray",
        7: "cock",
        8: "hen",
        9: "ostrich",
        10: "brambling",
        11: "goldfinch",
        12: "house finch",
        13: "junco",
        14: "indigo bunting",
        15: "robin",
        16: "bulbul",
        17: "jay",
        18: "magpie",
        19: "chickadee",
        20: "water ouzel",
        21: "kite",
        22: "bald eagle",
        23: "vulture",
        24: "great grey owl",
        25: "European fire salamander",
        26: "common newt",
        27: "eft",
        28: "spotted salamander",
        29: "axolotl",
        30: "bullfrog",
        31: "tree frog",
        32: "tailed frog",
        33: "loggerhead",
        34: "leatherback turtle",
        35: "mud turtle",
        36: "terrapin",
        37: "box turtle",
        38: "banded gecko",
        39: "common iguana",
        40: "American chameleon",
        41: "whiptail",
        42: "agama",
        43: "frilled lizard",
        44: "alligator lizard",
        45: "Gila monster",
        46: "green lizard",
        47: "African chameleon",
        48: "Komodo dragon",
        49: "African crocodile",
        50: "American alligator",
        51: "triceratops",
        52: "thunder snake",
        53: "ringneck snake",
        54: "hognose snake",
        55: "green snake",
        56: "king snake",
        57: "garter snake",
        58: "water snake",
        59: "vine snake",
        60: "night snake",
        61: "boa constrictor",
        62: "rock python",
        63: "Indian cobra",
        64: "green mamba",
        65: "sea snake",
        66: "horned viper",
        67: "diamondback",
        68: "sidewinder",
        69: "trilobite",
        70: "harvestman",
        71: "scorpion",
        72: "black and gold garden spider",
        73: "barn spider",
        74: "garden spider",
        75: "black widow",
        76: "tarantula",
        77: "wolf spider",
        78: "tick",
        79: "centipede",
        80: "black grouse",
        81: "ptarmigan",
        82: "ruffed grouse",
        83: "prairie chicken",
        84: "peacock",
        85: "quail",
        86: "partridge",
        87: "African grey",
        88: "macaw",
        89: "sulphur-crested cockatoo",
        90: "lorikeet",
        91: "coucal",
        92: "bee eater",
        93: "hornbill",
        94: "hummingbird",
        95: "jacamar",
        96: "toucan",
        97: "drake",
        98: "red-breasted merganser",
        99: "goose",
        100: "black swan",
        101: "tusker",
        102: "echidna",
        103: "platypus",
        104: "wallaby",
        105: "koala",
        106: "wombat",
        107: "jellyfish",
        108: "sea anemone",
        109: "brain coral",
        110: "flatworm",
        111: "nematode",
        112: "conch",
        113: "snail",
        114: "slug",
        115: "sea slug",
        116: "chiton",
        117: "chambered nautilus",
        118: "Dungeness crab",
        119: "rock crab",
        120: "fiddler crab",
        121: "king crab",
        122: "American lobster",
        123: "spiny lobster",
        124: "crayfish",
        125: "hermit crab",
        126: "isopod",
        127: "white stork",
        128: "black stork",
        129: "spoonbill",
        130: "flamingo",
        131: "little blue heron",
        132: "American egret",
        133: "bittern",
        134: "crane",
        135: "limpkin",
        136: "European gallinule",
        137: "American coot",
        138: "bustard",
        139: "ruddy turnstone",
        140: "red-backed sandpiper",
        141: "redshank",
        142: "dowitcher",
        143: "oystercatcher",
        144: "pelican",
        145: "king penguin",
        146: "albatross",
        147: "grey whale",
        148: "killer whale",
        149: "dugong",
        150: "sea lion",
        151: "Chihuahua",
        152: "Japanese spaniel",
        153: "Maltese dog",
        154: "Pekinese",
        155: "Shih-Tzu",
        156: "Blenheim spaniel",
        157: "papillon",
        158: "toy terrier",
        159: "Rhodesian ridgeback",
        160: "Afghan hound",
        161: "basset",
        162: "beagle",
        163: "bloodhound",
        164: "bluetick",
        165: "black-and-tan coonhound",
        166: "Walker hound",
        167: "English foxhound",
        168: "redbone",
        169: "borzoi",
        170: "Irish wolfhound",
        171: "Italian greyhound",
        172: "whippet",
        173: "Ibizan hound",
        174: "Norwegian elkhound",
        175: "otterhound",
        176: "Saluki",
        177: "Scottish deerhound",
        178: "Weimaraner",
        179: "Staffordshire bullterrier",
        180: "American Staffordshire terrier",
        181: "Bedlington terrier",
        182: "Border terrier",
        183: "Kerry blue terrier",
        184: "Irish terrier",
        185: "Norfolk terrier",
        186: "Norwich terrier",
        187: "Yorkshire terrier",
        188: "wire-haired fox terrier",
        189: "Lakeland terrier",
        190: "Sealyham terrier",
        191: "Airedale",
        192: "cairn",
        193: "Australian terrier",
        194: "Dandie Dinmont",
        195: "Boston bull",
        196: "miniature schnauzer",
        197: "giant schnauzer",
        198: "standard schnauzer",
        199: "Scotch terrier",
        200: "Tibetan terrier",
        201: "silky terrier",
        202: "soft-coated wheaten terrier",
        203: "West Highland white terrier",
        204: "Lhasa",
        205: "flat-coated retriever",
        206: "curly-coated retriever",
        207: "golden retriever",
        208: "Labrador retriever",
        209: "Chesapeake Bay retriever",
        210: "German short-haired pointer",
        211: "vizsla",
        212: "English setter",
        213: "Irish setter",
        214: "Gordon setter",
        215: "Brittany spaniel",
        216: "clumber",
        217: "English springer",
        218: "Welsh springer spaniel",
        219: "cocker spaniel",
        220: "Sussex spaniel",
        221: "Irish water spaniel",
        222: "kuvasz",
        223: "schipperke",
        224: "groenendael",
        225: "malinois",
        226: "briard",
        227: "kelpie",
        228: "komondor",
        229: "Old English sheepdog",
        230: "Shetland sheepdog",
        231: "collie",
        232: "Border collie",
        233: "Bouvier des Flandres",
        234: "Rottweiler",
        235: "German shepherd",
        236: "Doberman",
        237: "miniature pinscher",
        238: "Greater Swiss Mountain dog",
        239: "Bernese mountain dog",
        240: "Appenzeller",
        241: "EntleBucher",
        242: "boxer",
        243: "bull mastiff",
        244: "Tibetan mastiff",
        245: "French bulldog",
        246: "Great Dane",
        247: "Saint Bernard",
        248: "Eskimo dog",
        249: "malamute",
        250: "Siberian husky",
        251: "dalmatian",
        252: "affenpinscher",
        253: "basenji",
        254: "pug",
        255: "Leonberg",
        256: "Newfoundland",
        257: "Great Pyrenees",
        258: "Samoyed",
        259: "Pomeranian",
        260: "chow",
        261: "keeshond",
        262: "Brabancon griffon",
        263: "Pembroke",
        264: "Cardigan",
        265: "toy poodle",
        266: "miniature poodle",
        267: "standard poodle",
        268: "Mexican hairless",
        269: "timber wolf",
        270: "white wolf",
        271: "red wolf",
        272: "coyote",
        273: "dingo",
        274: "dhole",
        275: "African hunting dog",
        276: "hyena",
        277: "red fox",
        278: "kit fox",
        279: "Arctic fox",
        280: "grey fox",
        281: "tabby",
        282: "tiger cat",
        283: "Persian cat",
        284: "Siamese cat",
        285: "Egyptian cat",
        286: "cougar",
        287: "lynx",
        288: "leopard",
        289: "snow leopard",
        290: "jaguar",
        291: "lion",
        292: "tiger",
        293: "cheetah",
        294: "brown bear",
        295: "American black bear",
        296: "ice bear",
        297: "sloth bear",
        298: "mongoose",
        299: "meerkat",
        300: "tiger beetle",
        301: "ladybug",
        302: "ground beetle",
        303: "long-horned beetle",
        304: "leaf beetle",
        305: "dung beetle",
        306: "rhinoceros beetle",
        307: "weevil",
        308: "fly",
        309: "bee",
        310: "ant",
        311: "grasshopper",
        312: "cricket",
        313: "walking stick",
        314: "cockroach",
        315: "mantis",
        316: "cicada",
        317: "leafhopper",
        318: "lacewing",
        319: "dragonfly",
        320: "damselfly",
        321: "admiral",
        322: "ringlet",
        323: "monarch",
        324: "cabbage butterfly",
        325: "sulphur butterfly",
        326: "lycaenid",
        327: "starfish",
        328: "sea urchin",
        329: "sea cucumber",
        330: "wood rabbit",
        331: "hare",
        332: "Angora",
        333: "hamster",
        334: "porcupine",
        335: "fox squirrel",
        336: "marmot",
        337: "beaver",
        338: "guinea pig",
        339: "sorrel",
        340: "zebra",
        341: "hog",
        342: "wild boar",
        343: "warthog",
        344: "hippopotamus",
        345: "ox",
        346: "water buffalo",
        347: "bison",
        348: "ram",
        349: "bighorn",
        350: "ibex",
        351: "hartebeest",
        352: "impala",
        353: "gazelle",
        354: "Arabian camel",
        355: "llama",
        356: "weasel",
        357: "mink",
        358: "polecat",
        359: "black-footed ferret",
        360: "otter",
        361: "skunk",
        362: "badger",
        363: "armadillo",
        364: "three-toed sloth",
        365: "orangutan",
        366: "gorilla",
        367: "chimpanzee",
        368: "gibbon",
        369: "siamang",
        370: "guenon",
        371: "patas",
        372: "baboon",
        373: "macaque",
        374: "langur",
        375: "colobus",
        376: "proboscis monkey",
        377: "marmoset",
        378: "capuchin",
        379: "howler monkey",
        380: "titi",
        381: "spider monkey",
        382: "squirrel monkey",
        383: "Madagascar cat",
        384: "indri",
        385: "Indian elephant",
        386: "African elephant",
        387: "lesser panda",
        388: "giant panda",
        389: "barracouta",
        390: "eel",
        391: "coho",
        392: "rock beauty",
        393: "anemone fish",
        394: "sturgeon",
        395: "gar",
        396: "lionfish",
        397: "puffer",
        398: "abacus",
        399: "abaya",
        400: "academic gown",
        401: "accordion",
        402: "acoustic guitar",
        403: "aircraft carrier",
        404: "airliner",
        405: "airship",
        406: "altar",
        407: "ambulance",
        408: "amphibian",
        409: "analog clock",
        410: "apiary",
        411: "apron",
        412: "ashcan",
        413: "assault rifle",
        414: "backpack",
        415: "bakery",
        416: "balance beam",
        417: "balloon",
        418: "ballpoint",
        419: "Band Aid",
        420: "banjo",
        421: "bannister",
        422: "barbell",
        423: "barber chair",
        424: "barbershop",
        425: "barn",
        426: "barometer",
        427: "barrel",
        428: "barrow",
        429: "baseball",
        430: "basketball",
        431: "bassinet",
        432: "bassoon",
        433: "bathing cap",
        434: "bath towel",
        435: "bathtub",
        436: "beach wagon",
        437: "beacon",
        438: "beaker",
        439: "bearskin",
        440: "beer bottle",
        441: "beer glass",
        442: "bell cote",
        443: "bib",
        444: "bicycle-built-for-two",
        445: "bikini",
        446: "binder",
        447: "binoculars",
        448: "birdhouse",
        449: "boathouse",
        450: "bobsled",
        451: "bolo tie",
        452: "bonnet",
        453: "bookcase",
        454: "bookshop",
        455: "bottlecap",
        456: "bow",
        457: "bow tie",
        458: "brass",
        459: "brassiere",
        460: "breakwater",
        461: "breastplate",
        462: "broom",
        463: "bucket",
        464: "buckle",
        465: "bulletproof vest",
        466: "bullet train",
        467: "butcher shop",
        468: "cab",
        469: "caldron",
        470: "candle",
        471: "cannon",
        472: "canoe",
        473: "can opener",
        474: "cardigan",
        475: "car mirror",
        476: "carousel",
        477: "carpenters kit",
        478: "carton",
        479: "car wheel",
        480: "cash machine",
        481: "cassette",
        482: "cassette player",
        483: "castle",
        484: "catamaran",
        485: "CD player",
        486: "cello",
        487: "cellular telephone",
        488: "chain",
        489: "chainlink fence",
        490: "chain mail",
        491: "chain saw",
        492: "chest",
        493: "chiffonier",
        494: "chime",
        495: "china cabinet",
        496: "Christmas stocking",
        497: "church",
        498: "cinema",
        499: "cleaver",
        500: "cliff dwelling",
        501: "cloak",
        502: "clog",
        503: "cocktail shaker",
        504: "coffee mug",
        505: "coffeepot",
        506: "coil",
        507: "combination lock",
        508: "computer keyboard",
        509: "confectionery",
        510: "container ship",
        511: "convertible",
        512: "corkscrew",
        513: "cornet",
        514: "cowboy boot",
        515: "cowboy hat",
        516: "cradle",
        517: "crane",
        518: "crash helmet",
        519: "crate",
        520: "crib",
        521: "Crock Pot",
        522: "croquet ball",
        523: "crutch",
        524: "cuirass",
        525: "dam",
        526: "desk",
        527: "desktop computer",
        528: "dial telephone",
        529: "diaper",
        530: "digital clock",
        531: "digital watch",
        532: "dining table",
        533: "dishrag",
        534: "dishwasher",
        535: "disk brake",
        536: "dock",
        537: "dogsled",
        538: "dome",
        539: "doormat",
        540: "drilling platform",
        541: "drum",
        542: "drumstick",
        543: "dumbbell",
        544: "Dutch oven",
        545: "electric fan",
        546: "electric guitar",
        547: "electric locomotive",
        548: "entertainment center",
        549: "envelope",
        550: "espresso maker",
        551: "face powder",
        552: "feather boa",
        553: "file",
        554: "fireboat",
        555: "fire engine",
        556: "fire screen",
        557: "flagpole",
        558: "flute",
        559: "folding chair",
        560: "football helmet",
        561: "forklift",
        562: "fountain",
        563: "fountain pen",
        564: "four-poster",
        565: "freight car",
        566: "French horn",
        567: "frying pan",
        568: "fur coat",
        569: "garbage truck",
        570: "gasmask",
        571: "gas pump",
        572: "goblet",
        573: "go-kart",
        574: "golf ball",
        575: "golfcart",
        576: "gondola",
        577: "gong",
        578: "gown",
        579: "grand piano",
        580: "greenhouse",
        581: "grille",
        582: "grocery store",
        583: "guillotine",
        584: "hair slide",
        585: "hair spray",
        586: "half track",
        587: "hammer",
        588: "hamper",
        589: "hand blower",
        590: "hand-held computer",
        591: "handkerchief",
        592: "hard disc",
        593: "harmonica",
        594: "harp",
        595: "harvester",
        596: "hatchet",
        597: "holster",
        598: "home theater",
        599: "honeycomb",
        600: "hook",
        601: "hoopskirt",
        602: "horizontal bar",
        603: "horse cart",
        604: "hourglass",
        605: "iPod",
        606: "iron",
        607: "jack-o-lantern",
        608: "jean",
        609: "jeep",
        610: "jersey",
        611: "jigsaw puzzle",
        612: "jinrikisha",
        613: "joystick",
        614: "kimono",
        615: "knee pad",
        616: "knot",
        617: "lab coat",
        618: "ladle",
        619: "lampshade",
        620: "laptop",
        621: "lawn mower",
        622: "lens cap",
        623: "letter opener",
        624: "library",
        625: "lifeboat",
        626: "lighter",
        627: "limousine",
        628: "liner",
        629: "lipstick",
        630: "Loafer",
        631: "lotion",
        632: "loudspeaker",
        633: "loupe",
        634: "lumbermill",
        635: "magnetic compass",
        636: "mailbag",
        637: "mailbox",
        638: "maillot",
        639: "maillot",
        640: "manhole cover",
        641: "maraca",
        642: "marimba",
        643: "mask",
        644: "matchstick",
        645: "maypole",
        646: "maze",
        647: "measuring cup",
        648: "medicine chest",
        649: "megalith",
        650: "microphone",
        651: "microwave",
        652: "military uniform",
        653: "milk can",
        654: "minibus",
        655: "miniskirt",
        656: "minivan",
        657: "missile",
        658: "mitten",
        659: "mixing bowl",
        660: "mobile home",
        661: "Model T",
        662: "modem",
        663: "monastery",
        664: "monitor",
        665: "moped",
        666: "mortar",
        667: "mortarboard",
        668: "mosque",
        669: "mosquito net",
        670: "motor scooter",
        671: "mountain bike",
        672: "mountain tent",
        673: "mouse",
        674: "mousetrap",
        675: "moving van",
        676: "muzzle",
        677: "nail",
        678: "neck brace",
        679: "necklace",
        680: "nipple",
        681: "notebook",
        682: "obelisk",
        683: "oboe",
        684: "ocarina",
        685: "odometer",
        686: "oil filter",
        687: "organ",
        688: "oscilloscope",
        689: "overskirt",
        690: "oxcart",
        691: "oxygen mask",
        692: "packet",
        693: "paddle",
        694: "paddlewheel",
        695: "padlock",
        696: "paintbrush",
        697: "pajama",
        698: "palace",
        699: "panpipe",
        700: "paper towel",
        701: "parachute",
        702: "parallel bars",
        703: "park bench",
        704: "parking meter",
        705: "passenger car",
        706: "patio",
        707: "pay-phone",
        708: "pedestal",
        709: "pencil box",
        710: "pencil sharpener",
        711: "perfume",
        712: "Petri dish",
        713: "photocopier",
        714: "pick",
        715: "pickelhaube",
        716: "picket fence",
        717: "pickup",
        718: "pier",
        719: "piggy bank",
        720: "pill bottle",
        721: "pillow",
        722: "ping-pong ball",
        723: "pinwheel",
        724: "pirate",
        725: "pitcher",
        726: "plane",
        727: "planetarium",
        728: "plastic bag",
        729: "plate rack",
        730: "plow",
        731: "plunger",
        732: "Polaroid camera",
        733: "pole",
        734: "police van",
        735: "poncho",
        736: "pool table",
        737: "pop bottle",
        738: "pot",
        739: "potters wheel",
        740: "power drill",
        741: "prayer rug",
        742: "printer",
        743: "prison",
        744: "projectile",
        745: "projector",
        746: "puck",
        747: "punching bag",
        748: "purse",
        749: "quill",
        750: "quilt",
        751: "racer",
        752: "racket",
        753: "radiator",
        754: "radio",
        755: "radio telescope",
        756: "rain barrel",
        757: "recreational vehicle",
        758: "reel",
        759: "reflex camera",
        760: "refrigerator",
        761: "remote control",
        762: "restaurant",
        763: "revolver",
        764: "rifle",
        765: "rocking chair",
        766: "rotisserie",
        767: "rubber eraser",
        768: "rugby ball",
        769: "rule",
        770: "running shoe",
        771: "safe",
        772: "safety pin",
        773: "saltshaker",
        774: "sandal",
        775: "sarong",
        776: "sax",
        777: "scabbard",
        778: "scale",
        779: "school bus",
        780: "schooner",
        781: "scoreboard",
        782: "screen",
        783: "screw",
        784: "screwdriver",
        785: "seat belt",
        786: "sewing machine",
        787: "shield",
        788: "shoe shop",
        789: "shoji",
        790: "shopping basket",
        791: "shopping cart",
        792: "shovel",
        793: "shower cap",
        794: "shower curtain",
        795: "ski",
        796: "ski mask",
        797: "sleeping bag",
        798: "slide rule",
        799: "sliding door",
        800: "slot",
        801: "snorkel",
        802: "snowmobile",
        803: "snowplow",
        804: "soap dispenser",
        805: "soccer ball",
        806: "sock",
        807: "solar dish",
        808: "sombrero",
        809: "soup bowl",
        810: "space bar",
        811: "space heater",
        812: "space shuttle",
        813: "spatula",
        814: "speedboat",
        815: "spider web",
        816: "spindle",
        817: "sports car",
        818: "spotlight",
        819: "stage",
        820: "steam locomotive",
        821: "steel arch bridge",
        822: "steel drum",
        823: "stethoscope",
        824: "stole",
        825: "stone wall",
        826: "stopwatch",
        827: "stove",
        828: "strainer",
        829: "streetcar",
        830: "stretcher",
        831: "studio couch",
        832: "stupa",
        833: "submarine",
        834: "suit",
        835: "sundial",
        836: "sunglass",
        837: "sunglasses",
        838: "sunscreen",
        839: "suspension bridge",
        840: "swab",
        841: "sweatshirt",
        842: "swimming trunks",
        843: "swing",
        844: "switch",
        845: "syringe",
        846: "table lamp",
        847: "tank",
        848: "tape player",
        849: "teapot",
        850: "teddy",
        851: "television",
        852: "tennis ball",
        853: "thatch",
        854: "theater curtain",
        855: "thimble",
        856: "thresher",
        857: "throne",
        858: "tile roof",
        859: "toaster",
        860: "tobacco shop",
        861: "toilet seat",
        862: "torch",
        863: "totem pole",
        864: "tow truck",
        865: "toyshop",
        866: "tractor",
        867: "trailer truck",
        868: "tray",
        869: "trench coat",
        870: "tricycle",
        871: "trimaran",
        872: "tripod",
        873: "triumphal arch",
        874: "trolleybus",
        875: "trombone",
        876: "tub",
        877: "turnstile",
        878: "typewriter keyboard",
        879: "umbrella",
        880: "unicycle",
        881: "upright",
        882: "vacuum",
        883: "vase",
        884: "vault",
        885: "velvet",
        886: "vending machine",
        887: "vestment",
        888: "viaduct",
        889: "violin",
        890: "volleyball",
        891: "waffle iron",
        892: "wall clock",
        893: "wallet",
        894: "wardrobe",
        895: "warplane",
        896: "washbasin",
        897: "washer",
        898: "water bottle",
        899: "water jug",
        900: "water tower",
        901: "whiskey jug",
        902: "whistle",
        903: "wig",
        904: "window screen",
        905: "window shade",
        906: "Windsor tie",
        907: "wine bottle",
        908: "wing",
        909: "wok",
        910: "wooden spoon",
        911: "wool",
        912: "worm fence",
        913: "wreck",
        914: "yawl",
        915: "yurt",
        916: "web site",
        917: "comic book",
        918: "crossword puzzle",
        919: "street sign",
        920: "traffic light",
        921: "book jacket",
        922: "menu",
        923: "plate",
        924: "guacamole",
        925: "consomme",
        926: "hot pot",
        927: "trifle",
        928: "ice cream",
        929: "ice lolly",
        930: "French loaf",
        931: "bagel",
        932: "pretzel",
        933: "cheeseburger",
        934: "hotdog",
        935: "mashed potato",
        936: "head cabbage",
        937: "broccoli",
        938: "cauliflower",
        939: "zucchini",
        940: "spaghetti squash",
        941: "acorn squash",
        942: "butternut squash",
        943: "cucumber",
        944: "artichoke",
        945: "bell pepper",
        946: "cardoon",
        947: "mushroom",
        948: "Granny Smith",
        949: "strawberry",
        950: "orange",
        951: "lemon",
        952: "fig",
        953: "pineapple",
        954: "banana",
        955: "jackfruit",
        956: "custard apple",
        957: "pomegranate",
        958: "hay",
        959: "carbonara",
        960: "chocolate sauce",
        961: "dough",
        962: "meat loaf",
        963: "pizza",
        964: "potpie",
        965: "burrito",
        966: "red wine",
        967: "espresso",
        968: "cup",
        969: "eggnog",
        970: "alp",
        971: "bubble",
        972: "cliff",
        973: "coral reef",
        974: "geyser",
        975: "lakeside",
        976: "promontory",
        977: "sandbar",
        978: "seashore",
        979: "valley",
        980: "volcano",
        981: "ballplayer",
        982: "groom",
        983: "scuba diver",
        984: "rapeseed",
        985: "daisy",
        986: "yellow ladys slipper",
        987: "corn",
        988: "acorn",
        989: "hip",
        990: "buckeye",
        991: "coral fungus",
        992: "agaric",
        993: "gyromitra",
        994: "stinkhorn",
        995: "earthstar",
        996: "hen-of-the-woods",
        997: "bolete",
        998: "ear",
        999: "toilet tissue"
    }

}