1796 lines
57 KiB
Python
1796 lines
57 KiB
Python
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"
|
||
}
|
||
|
||
}
|