eurecom-ds/scoresdeve-ema-celeba-64
Unconditional Image Generation
•
Updated
•
32.6k
•
1
image
imagewidth (px) 178
178
| attributes
sequence | identity
int64 1
10.2k
| bbox
sequence | landmarks
sequence |
---|---|---|---|---|
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[
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0,
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1,
1,
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1,
0,
0,
0,
0,
0,
0,
0,
0,
1,
0,
0,
1,
0,
0,
1
] | 5,512 | [
322,
138,
146,
202
] | [
69,
111,
108,
111,
88,
135,
73,
152,
104,
152
] |
|
[
0,
0,
0,
1,
0,
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1,
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0,
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0,
0,
0,
0,
0,
1,
0,
0,
0,
0,
0,
0,
1
] | 3,272 | [
102,
56,
125,
173
] | [
74,
106,
101,
111,
69,
138,
80,
156,
102,
158
] |
|
[
0,
0,
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1,
0,
0,
0,
0,
1,
0,
0,
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1
] | 8,744 | [
96,
74,
174,
241
] | [
72,
110,
105,
110,
94,
132,
76,
155,
101,
155
] |
|
[
0,
0,
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1,
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0,
0,
0,
0,
0,
0,
1,
0,
0,
0,
0,
0,
0,
0,
1
] | 4,407 | [
128,
99,
242,
335
] | [
69,
112,
109,
111,
93,
137,
68,
151,
109,
152
] |
|
[
0,
1,
1,
0,
0,
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1,
0,
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1,
0,
0,
0,
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0,
0,
1,
0,
0,
0,
1,
0,
1,
1,
0,
1,
0,
0,
1
] | 6,446 | [
167,
207,
96,
133
] | [
69,
111,
108,
111,
100,
131,
68,
151,
108,
152
] |
|
[
0,
0,
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0,
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1,
0,
0,
0,
0,
0,
0,
0,
0,
1,
0,
1,
0,
0,
1
] | 2,088 | [
91,
87,
166,
230
] | [
69,
112,
108,
111,
89,
139,
73,
152,
103,
152
] |
|
[
1,
0,
1,
0,
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1,
0,
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1,
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0,
0,
0,
0,
0,
1,
0,
0,
0,
0,
0,
0,
0,
1
] | 1,058 | [
127,
91,
172,
238
] | [
70,
105,
102,
114,
65,
144,
73,
151,
112,
159
] |
|
[
0,
1,
1,
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1,
0,
1,
0,
1,
0,
1,
0,
0,
1,
0,
1,
0,
0,
1
] | 5,230 | [
84,
102,
174,
241
] | [
68,
112,
109,
112,
92,
138,
68,
152,
109,
150
] |
|
[
0,
1,
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1,
0,
0,
0,
1,
0,
1,
1,
0,
1,
0,
0,
1
] | 5,239 | [
133,
62,
133,
184
] | [
68,
111,
109,
112,
90,
136,
67,
150,
110,
151
] |
|
[
0,
0,
0,
1,
0,
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1,
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0,
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0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1,
1,
0,
1
] | 2,079 | [
743,
626,
998,
1382
] | [
71,
110,
106,
111,
87,
137,
73,
153,
105,
154
] |
|
[
0,
1,
1,
0,
0,
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1,
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1,
0,
0,
0,
0,
0,
1,
0,
1,
1,
0,
1,
0,
0,
1
] | 4,125 | [
220,
26,
138,
191
] | [
69,
112,
108,
111,
86,
140,
68,
151,
108,
152
] |
|
[
0,
0,
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1,
0,
0,
0,
1,
0,
1,
0,
0,
1,
0,
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1
] | 6,318 | [
134,
65,
99,
137
] | [
66,
112,
110,
113,
87,
139,
69,
149,
109,
150
] |
|
[
0,
1,
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0,
0,
0,
0,
0,
1,
1,
0,
1,
0,
1,
0,
0,
1
] | 9,177 | [
142,
89,
203,
281
] | [
68,
112,
109,
112,
88,
135,
72,
151,
105,
152
] |
|
[
0,
0,
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0,
0,
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0,
0,
0,
0,
0,
0,
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] | 4,310 | [
264,
58,
140,
194
] | [
70,
113,
108,
110,
81,
125,
74,
154,
100,
150
] |
|
[
0,
0,
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1,
1,
0,
0,
0,
0,
0,
0,
0,
0,
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0
] | 6,817 | [
47,
45,
60,
83
] | [
73,
107,
102,
111,
76,
139,
78,
157,
102,
156
] |
|
[
0,
0,
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1,
0,
0,
0,
0,
0,
1,
0,
0,
0,
0,
0,
0,
0,
1
] | 1,058 | [
110,
30,
133,
184
] | [
68,
112,
109,
111,
93,
139,
66,
151,
110,
151
] |
|
[
1,
0,
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1,
1,
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0,
1,
0,
0,
0,
0,
0,
0,
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] | 1,446 | [
61,
98,
255,
353
] | [
67,
113,
110,
112,
83,
140,
69,
148,
107,
152
] |
|
[
0,
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0,
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0,
1,
0,
0,
0,
0,
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] | 3,896 | [
135,
159,
333,
461
] | [
70,
111,
107,
111,
85,
135,
72,
153,
105,
153
] |
|
[
1,
0,
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] | 9,397 | [
35,
64,
91,
126
] | [
70,
112,
108,
111,
91,
137,
72,
153,
103,
152
] |
|
[
0,
1,
1,
1,
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1,
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0,
0,
0,
0,
0,
1,
0,
1,
0,
0,
1,
1,
0,
1
] | 8,443 | [
876,
228,
224,
310
] | [
67,
112,
110,
112,
87,
138,
66,
150,
111,
151
] |
|
[
0,
0,
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0,
0,
0,
0,
0,
0,
1,
0,
0,
0,
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1,
0
] | 4,883 | [
30,
62,
268,
371
] | [
69,
111,
108,
111,
86,
133,
73,
151,
104,
153
] |
|
[
0,
1,
1,
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0,
0,
0,
0,
0,
0,
0,
1,
0,
0,
0,
1,
0,
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] | 545 | [
188,
91,
281,
389
] | [
69,
111,
107,
111,
97,
133,
74,
152,
103,
152
] |
|
[
0,
1,
1,
0,
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0,
0,
0,
0,
0,
0,
0,
0,
0,
1,
0,
1,
0,
0,
1
] | 10,144 | [
220,
25,
114,
158
] | [
71,
111,
106,
110,
95,
134,
73,
154,
103,
154
] |
|
[
0,
1,
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0,
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0,
0,
0,
0,
0,
0,
0,
1,
0,
0,
1,
0,
0,
1
] | 2,879 | [
167,
64,
140,
194
] | [
68,
112,
109,
112,
89,
132,
73,
152,
104,
150
] |
|
[
0,
0,
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0,
0,
0,
0,
0,
0,
0,
0,
0,
1,
0,
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1
] | 6,204 | [
125,
169,
263,
364
] | [
72,
110,
105,
110,
75,
132,
75,
153,
101,
156
] |
|
[
0,
0,
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0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1
] | 6,880 | [
17,
84,
174,
241
] | [
71,
112,
107,
109,
74,
131,
70,
154,
104,
153
] |
|
[
1,
0,
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0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
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1
] | 3,316 | [
171,
50,
239,
331
] | [
74,
110,
104,
108,
97,
130,
78,
158,
97,
154
] |
|
[
0,
0,
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0,
0,
0,
0,
1,
0,
0,
0,
0,
1,
1,
0,
1
] | 1,499 | [
263,
68,
94,
130
] | [
69,
111,
108,
112,
87,
137,
72,
153,
105,
151
] |
|
[
0,
1,
1,
0,
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0,
0,
0,
0,
1,
0,
1,
0,
1,
0,
0,
1,
0,
0,
1
] | 1,539 | [
1,
58,
91,
126
] | [
68,
111,
108,
113,
82,
142,
66,
150,
112,
152
] |
|
[
1,
0,
1,
0,
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0,
0,
0,
0,
0,
1,
1,
0,
0,
0,
0,
0,
0,
1
] | 3,629 | [
82,
66,
177,
245
] | [
68,
111,
108,
112,
88,
132,
71,
151,
106,
152
] |
|
[
0,
0,
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0,
0,
0,
0,
0,
1,
1,
0,
0,
0,
0,
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1
] | 3,046 | [
377,
105,
164,
227
] | [
69,
112,
108,
111,
87,
136,
71,
152,
106,
151
] |
|
[
0,
1,
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0,
0,
0,
0,
0,
0,
1,
0,
0,
1,
0,
1,
1,
0,
0
] | 3,792 | [
29,
39,
57,
79
] | [
66,
112,
110,
113,
86,
136,
65,
149,
114,
150
] |
|
[
0,
0,
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0,
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0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0
] | 1,175 | [
254,
95,
237,
322
] | [
70,
110,
106,
111,
93,
136,
73,
154,
104,
153
] |
|
[
0,
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0,
0,
0,
0,
1,
0,
0,
0,
1,
0,
0,
0,
0
] | 5,300 | [
98,
103,
234,
324
] | [
69,
111,
108,
111,
93,
136,
72,
151,
105,
153
] |
|
[
0,
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58,
54,
229,
317
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68,
111,
108,
112,
88,
126,
77,
151,
101,
152
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[
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46,
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[
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20,
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[
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145,
50,
161,
223
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70,
110,
107,
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[
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1,
1,
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126,
1,
374,
518
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111,
108,
113,
88,
124,
75,
153,
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[
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1,
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1,
0,
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0,
0,
0,
1,
0,
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257,
6,
99,
137
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70,
111,
107,
111,
80,
129,
73,
152,
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[
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175,
29,
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67,
113,
110,
112,
83,
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67,
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70,
116,
221,
306
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83,
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[
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272,
43,
151,
209
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66,
112,
111,
113,
90,
126,
70,
149,
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[
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57,
47,
221,
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70,
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84,
79,
281,
389
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111,
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88,
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271,
312,
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835
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[
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44,
34,
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52,
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385
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337,
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443
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38,
33,
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133,
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[
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101,
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241
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139,
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133,
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194
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167,
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148,
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28,
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70,
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132,
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Porting of the famous celeba dataset to 🤗 Datasets.
attr
)
attributes
feature consists of binary labels that represent the presence or absence of 40 different facial attributes. Each attribute is encoded as either 0 (absence) or 1 (presence). These attributes cover a wide range of facial characteristics and styles, such as "Smiling", "Wearing Hat", "Eyeglasses", etc.40
int8
identity
)
identity
feature represents the label for each individual in the dataset. It is used to identify which images belong to the same person. This allows for tasks such as face recognition and verification, where the goal is to match different images of the same person.int64
bbox
)
bounding box
feature provides the coordinates for a rectangle that encapsulates the face in each image. This is useful for tasks where the face needs to be isolated or focused upon. The bounding box is defined by four integers: the x and y coordinates of the top-left corner, followed by the width and height of the box.4
int32
[x, y, width, height]
, where (x, y)
are the coordinates of the top-left corner of the bounding box.landmarks
)
landmarks
feature specifies the coordinates of key facial points, which are crucial for detailed facial analysis and tasks like advanced face manipulation or animation. These landmarks identify the positions of critical facial components such as the eyes, nose, and mouth.10
int32
[lefteye_x, lefteye_y, righteye_x, righteye_y, nose_x, nose_y, leftmouth_x, leftmouth_y, rightmouth_x, rightmouth_y]
, representing the x and y coordinates of each landmark point.Script used for porting:
import torchvision
from datasets import Features, Dataset, Image as HFImage, ClassLabel, Sequence, Value
import numpy as np
celeba_dataset = torchvision.datasets.CelebA(root="./celeb_a", split="train",
target_type=["attr", "identity", "bbox", "landmarks"], download=False)
def gen():
for img, (attr, identity, bbox, landmarks) in celeba_dataset:
yield {
"image": img,
"attributes": attr.numpy(),
"identity": identity.item(),
"bbox": bbox.numpy(),
"landmarks": landmarks.numpy()
}
features = Features({
'image': HFImage(decode=True, id=None),
'attributes': Sequence(feature=Value("int8"), length=40),
'identity': Value("int64"),
'bbox': Sequence(feature=Value("int32"), length=4),
'landmarks': Sequence(feature=Value("int32"), length=10)
})
# Create a Dataset object from the generator
hf_dataset = Dataset.from_generator(generator=gen, features=features)
# Push the dataset to the Hugging Face Hub
hf_dataset.push_to_hub("eurecom-ds/celeba", split="train")