Hugging Face's logo

Datasets:
cgarciae
/
cartoonset

Modalities:
Tabular
Size:
100K - 1M
ArXiv:
Libraries:
Datasets
License:
Dataset card Data Studio Files
xet
Community
3
cgarciae commited on
Commit
e6a3ac4
·
1 Parent(s): 29bb837

add support for loading features

Browse files
Files changed (3) hide show
  1. README.md +31 -1
  2. cartoonset.py +108 -16
  3. dataset_infos.json +452 -0
README.md CHANGED
@@ -42,7 +42,7 @@ import datasets
42
  from io import BytesIO
43
  from PIL import Image
44
 
45
- ds = datasets.load_dataset("cgarciae/cartoonset", "10k") # or "100k"
46
 
47
  def process_fn(sample):
48
  img = Image.open(BytesIO(sample["img_bytes"]))
@@ -74,6 +74,36 @@ def process_fn(sample):
74
  ds = ds.map(process_fn)
75
  ```
76
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
77
  ## Dataset Structure
78
  ### Data Instances
79
  A sample from the training set is provided below:
 
42
  from io import BytesIO
43
  from PIL import Image
44
 
45
+ ds = datasets.load_dataset("cgarciae/cartoonset", "10k")
46
 
47
  def process_fn(sample):
48
  img = Image.open(BytesIO(sample["img_bytes"]))
 
74
  ds = ds.map(process_fn)
75
  ```
76
 
77
+ **Additional features:**
78
+ You can also access the features that generated each sample e.g:
79
+
80
+ ```python
81
+ ds = datasets.load_dataset("cgarciae/cartoonset", "10k+features") # or "100k+features"
82
+ ```
83
+
84
+ Apart from `img_bytes` these configurations add a total of 18 * 2 additional `int` features, these come in `{feature}`, `{feature}_num_categories` pairs where `num_categories` indicates the number of categories for that feature:
85
+
86
+ ```
87
+ eye_angle, eye_angle_num_categories
88
+ eye_lashes, eye_lashes_num_categories
89
+ eye_lid, eye_lid_num_categories
90
+ chin_length, chin_length_num_categories
91
+ eyebrow_weight, eyebrow_weight_num_categories
92
+ eyebrow_shape, eyebrow_shape_num_categories
93
+ eyebrow_thickness, eyebrow_thickness_num_categories
94
+ face_shape, face_shape_num_categories
95
+ facial_hair, facial_hair_num_categories
96
+ hair, hair_num_categories
97
+ eye_color, eye_color_num_categories
98
+ face_color, face_color_num_categories
99
+ hair_color, hair_color_num_categories
100
+ glasses, glasses_num_categories
101
+ glasses_color, glasses_color_num_categories
102
+ eye_slant, eye_slant_num_categories
103
+ eyebrow_width, eyebrow_width_num_categories
104
+ eye_eyebrow_distance, eye_eyebrow_distance_num_categories
105
+ ```
106
+
107
  ## Dataset Structure
108
  ### Data Instances
109
  A sample from the training set is provided below:
cartoonset.py CHANGED
@@ -1,15 +1,14 @@
1
  """Cartoonset-10k Data Set"""
2
 
3
 
4
- import pickle
 
5
 
6
- import numpy as np
7
- import PIL.Image
8
  import tarfile
 
9
 
10
 
11
  import datasets
12
- from datasets.tasks import ImageClassification
13
 
14
 
15
  _CITATION = r"""
@@ -53,26 +52,75 @@ class Cartoonset(datasets.GeneratorBasedBuilder):
53
  datasets.BuilderConfig(
54
  name="10k",
55
  version=datasets.Version("1.0.0", ""),
56
- description="Loads the Cartoonset-10k Data Set",
 
 
 
 
 
57
  ),
58
  datasets.BuilderConfig(
59
  name="100k",
60
  version=datasets.Version("1.0.0", ""),
61
- description="Loads the Cartoonset-100k Data Set",
 
 
 
 
 
62
  ),
63
  ]
64
 
65
  DEFAULT_CONFIG_NAME = "10k"
66
 
67
  def _info(self):
68
- return datasets.DatasetInfo(
69
- description=_DESCRIPTION,
70
- features=datasets.Features(
 
71
  {
72
- # "img": datasets.Image(),
73
- "img_bytes": datasets.Value("binary"),
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
74
  }
75
- ),
 
 
 
 
76
  supervised_keys=("img_bytes",),
77
  homepage="https://www.cs.toronto.edu/~kriz/cifar.html",
78
  citation=_CITATION,
@@ -80,7 +128,7 @@ class Cartoonset(datasets.GeneratorBasedBuilder):
80
 
81
  def _split_generators(self, dl_manager: datasets.DownloadManager):
82
 
83
- url = _DATA_URLS[self.config.name]
84
  archive = dl_manager.download(url)
85
 
86
  return [
@@ -96,13 +144,57 @@ class Cartoonset(datasets.GeneratorBasedBuilder):
96
  def _generate_examples(self, files, split):
97
  """This function returns the examples in the raw (text) form."""
98
 
 
 
 
 
 
 
99
  path: str
100
  file_obj: tarfile.ExFileObject
 
101
  for path, file_obj in files:
 
102
 
103
  if path.endswith(".png"):
104
  image = file_obj.read()
105
 
106
- yield path, {
107
- "img_bytes": image,
108
- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
  """Cartoonset-10k Data Set"""
2
 
3
 
4
+ from io import BytesIO
5
+ from typing import Optional
6
 
 
 
7
  import tarfile
8
+ import pandas as pd
9
 
10
 
11
  import datasets
 
12
 
13
 
14
  _CITATION = r"""
 
52
  datasets.BuilderConfig(
53
  name="10k",
54
  version=datasets.Version("1.0.0", ""),
55
+ description="Loads the Cartoonset-10k Data Set (images only).",
56
+ ),
57
+ datasets.BuilderConfig(
58
+ name="10k+features",
59
+ version=datasets.Version("1.0.0", ""),
60
+ description="Loads the Cartoonset-10k Data Set (images and attributes).",
61
  ),
62
  datasets.BuilderConfig(
63
  name="100k",
64
  version=datasets.Version("1.0.0", ""),
65
+ description="Loads the Cartoonset-100k Data Set (images only).",
66
+ ),
67
+ datasets.BuilderConfig(
68
+ name="100k+features",
69
+ version=datasets.Version("1.0.0", ""),
70
+ description="Loads the Cartoonset-100k Data Set (images and attributes).",
71
  ),
72
  ]
73
 
74
  DEFAULT_CONFIG_NAME = "10k"
75
 
76
  def _info(self):
77
+ features = {"img_bytes": datasets.Value("binary")}
78
+
79
+ if self.config.name.endswith("+features"):
80
+ features.update(
81
  {
82
+ "eye_angle": datasets.Value("int32"),
83
+ "eye_angle_num_categories": datasets.Value("int32"),
84
+ "eye_lashes": datasets.Value("int32"),
85
+ "eye_lashes_num_categories": datasets.Value("int32"),
86
+ "eye_lid": datasets.Value("int32"),
87
+ "eye_lid_num_categories": datasets.Value("int32"),
88
+ "chin_length": datasets.Value("int32"),
89
+ "chin_length_num_categories": datasets.Value("int32"),
90
+ "eyebrow_weight": datasets.Value("int32"),
91
+ "eyebrow_weight_num_categories": datasets.Value("int32"),
92
+ "eyebrow_shape": datasets.Value("int32"),
93
+ "eyebrow_shape_num_categories": datasets.Value("int32"),
94
+ "eyebrow_thickness": datasets.Value("int32"),
95
+ "eyebrow_thickness_num_categories": datasets.Value("int32"),
96
+ "face_shape": datasets.Value("int32"),
97
+ "face_shape_num_categories": datasets.Value("int32"),
98
+ "facial_hair": datasets.Value("int32"),
99
+ "facial_hair_num_categories": datasets.Value("int32"),
100
+ "hair": datasets.Value("int32"),
101
+ "hair_num_categories": datasets.Value("int32"),
102
+ "eye_color": datasets.Value("int32"),
103
+ "eye_color_num_categories": datasets.Value("int32"),
104
+ "face_color": datasets.Value("int32"),
105
+ "face_color_num_categories": datasets.Value("int32"),
106
+ "hair_color": datasets.Value("int32"),
107
+ "hair_color_num_categories": datasets.Value("int32"),
108
+ "glasses": datasets.Value("int32"),
109
+ "glasses_num_categories": datasets.Value("int32"),
110
+ "glasses_color": datasets.Value("int32"),
111
+ "glasses_color_num_categories": datasets.Value("int32"),
112
+ "eye_slant": datasets.Value("int32"),
113
+ "eye_slant_num_categories": datasets.Value("int32"),
114
+ "eyebrow_width": datasets.Value("int32"),
115
+ "eyebrow_width_num_categories": datasets.Value("int32"),
116
+ "eye_eyebrow_distance": datasets.Value("int32"),
117
+ "eye_eyebrow_distance_num_categories": datasets.Value("int32"),
118
  }
119
+ )
120
+
121
+ return datasets.DatasetInfo(
122
+ description=_DESCRIPTION,
123
+ features=datasets.Features(features),
124
  supervised_keys=("img_bytes",),
125
  homepage="https://www.cs.toronto.edu/~kriz/cifar.html",
126
  citation=_CITATION,
 
128
 
129
  def _split_generators(self, dl_manager: datasets.DownloadManager):
130
 
131
+ url = _DATA_URLS[self.config.name.replace("+features", "")]
132
  archive = dl_manager.download(url)
133
 
134
  return [
 
144
  def _generate_examples(self, files, split):
145
  """This function returns the examples in the raw (text) form."""
146
 
147
+ if self.config.name.endswith("+features"):
148
+ return self._generate_examples_with_features(files, split)
149
+ else:
150
+ return self._generate_examples_without_features(files, split)
151
+
152
+ def _generate_examples_without_features(self, files, split):
153
  path: str
154
  file_obj: tarfile.ExFileObject
155
+ root: str
156
  for path, file_obj in files:
157
+ root = path[:-4]
158
 
159
  if path.endswith(".png"):
160
  image = file_obj.read()
161
 
162
+ yield root, {"img_bytes": image}
163
+
164
+ def _generate_examples_with_features(self, files, split):
165
+ path: str
166
+ file_obj: tarfile.ExFileObject
167
+ outputs = {}
168
+ root: Optional[str] = None
169
+ for path, file_obj in files:
170
+ root = path[:-4]
171
+
172
+ if root not in outputs:
173
+ outputs[root] = {}
174
+
175
+ current_output = outputs[root]
176
+
177
+ if path.endswith(".png"):
178
+ image = file_obj.read()
179
+
180
+ current_output["img_bytes"] = image
181
+ else:
182
+ df = pd.read_csv(
183
+ BytesIO(file_obj.read()),
184
+ header=None,
185
+ names=["feature", "value", "num_categories"],
186
+ )
187
+
188
+ for index, row in df.iterrows():
189
+ current_output[row.feature] = row.value
190
+ current_output[f"{row.feature}_num_categories"] = row.num_categories
191
+
192
+ if "img_bytes" in current_output and len(current_output) > 1:
193
+ yield root, current_output
194
+ del outputs[root]
195
+ root = None
196
+
197
+ if len(outputs) > 0:
198
+ raise ValueError(
199
+ f"Unable to extract the following samples: {list(outputs)}"
200
+ )
dataset_infos.json CHANGED
@@ -90,5 +90,457 @@
90
  "post_processing_size": null,
91
  "dataset_size": 4889166989,
92
  "size_in_bytes": 9655609025
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
93
  }
94
  }
 
90
  "post_processing_size": null,
91
  "dataset_size": 4889166989,
92
  "size_in_bytes": 9655609025
93
+ },
94
+ "10k+features": {
95
+ "description": "Cartoon Set is a collection of random, 2D cartoon avatar images. The cartoons vary in 10 artwork \ncategories, 4 color categories, and 4 proportion categories, with a total of ~1013 possible \ncombinations. We provide sets of 10k and 100k randomly chosen cartoons and labeled attributes. \n",
96
+ "citation": "\n@article{DBLP:journals/corr/abs-1711-05139,\n author = {Amelie Royer and\n Konstantinos Bousmalis and\n Stephan Gouws and\n Fred Bertsch and\n Inbar Mosseri and\n Forrester Cole and\n Kevin Murphy},\n title = {{XGAN:} Unsupervised Image-to-Image Translation for many-to-many Mappings},\n journal = {CoRR},\n volume = {abs/1711.05139},\n year = {2017},\n url = {http://arxiv.org/abs/1711.05139},\n eprinttype = {arXiv},\n eprint = {1711.05139},\n timestamp = {Mon, 13 Aug 2018 16:47:38 +0200},\n biburl = {https://dblp.org/rec/journals/corr/abs-1711-05139.bib},\n bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n",
97
+ "homepage": "https://www.cs.toronto.edu/~kriz/cifar.html",
98
+ "license": "",
99
+ "features": {
100
+ "img_bytes": {
101
+ "dtype": "binary",
102
+ "id": null,
103
+ "_type": "Value"
104
+ },
105
+ "eye_angle": {
106
+ "dtype": "int32",
107
+ "id": null,
108
+ "_type": "Value"
109
+ },
110
+ "eye_angle_num_categories": {
111
+ "dtype": "int32",
112
+ "id": null,
113
+ "_type": "Value"
114
+ },
115
+ "eye_lashes": {
116
+ "dtype": "int32",
117
+ "id": null,
118
+ "_type": "Value"
119
+ },
120
+ "eye_lashes_num_categories": {
121
+ "dtype": "int32",
122
+ "id": null,
123
+ "_type": "Value"
124
+ },
125
+ "eye_lid": {
126
+ "dtype": "int32",
127
+ "id": null,
128
+ "_type": "Value"
129
+ },
130
+ "eye_lid_num_categories": {
131
+ "dtype": "int32",
132
+ "id": null,
133
+ "_type": "Value"
134
+ },
135
+ "chin_length": {
136
+ "dtype": "int32",
137
+ "id": null,
138
+ "_type": "Value"
139
+ },
140
+ "chin_length_num_categories": {
141
+ "dtype": "int32",
142
+ "id": null,
143
+ "_type": "Value"
144
+ },
145
+ "eyebrow_weight": {
146
+ "dtype": "int32",
147
+ "id": null,
148
+ "_type": "Value"
149
+ },
150
+ "eyebrow_weight_num_categories": {
151
+ "dtype": "int32",
152
+ "id": null,
153
+ "_type": "Value"
154
+ },
155
+ "eyebrow_shape": {
156
+ "dtype": "int32",
157
+ "id": null,
158
+ "_type": "Value"
159
+ },
160
+ "eyebrow_shape_num_categories": {
161
+ "dtype": "int32",
162
+ "id": null,
163
+ "_type": "Value"
164
+ },
165
+ "eyebrow_thickness": {
166
+ "dtype": "int32",
167
+ "id": null,
168
+ "_type": "Value"
169
+ },
170
+ "eyebrow_thickness_num_categories": {
171
+ "dtype": "int32",
172
+ "id": null,
173
+ "_type": "Value"
174
+ },
175
+ "face_shape": {
176
+ "dtype": "int32",
177
+ "id": null,
178
+ "_type": "Value"
179
+ },
180
+ "face_shape_num_categories": {
181
+ "dtype": "int32",
182
+ "id": null,
183
+ "_type": "Value"
184
+ },
185
+ "facial_hair": {
186
+ "dtype": "int32",
187
+ "id": null,
188
+ "_type": "Value"
189
+ },
190
+ "facial_hair_num_categories": {
191
+ "dtype": "int32",
192
+ "id": null,
193
+ "_type": "Value"
194
+ },
195
+ "hair": {
196
+ "dtype": "int32",
197
+ "id": null,
198
+ "_type": "Value"
199
+ },
200
+ "hair_num_categories": {
201
+ "dtype": "int32",
202
+ "id": null,
203
+ "_type": "Value"
204
+ },
205
+ "eye_color": {
206
+ "dtype": "int32",
207
+ "id": null,
208
+ "_type": "Value"
209
+ },
210
+ "eye_color_num_categories": {
211
+ "dtype": "int32",
212
+ "id": null,
213
+ "_type": "Value"
214
+ },
215
+ "face_color": {
216
+ "dtype": "int32",
217
+ "id": null,
218
+ "_type": "Value"
219
+ },
220
+ "face_color_num_categories": {
221
+ "dtype": "int32",
222
+ "id": null,
223
+ "_type": "Value"
224
+ },
225
+ "hair_color": {
226
+ "dtype": "int32",
227
+ "id": null,
228
+ "_type": "Value"
229
+ },
230
+ "hair_color_num_categories": {
231
+ "dtype": "int32",
232
+ "id": null,
233
+ "_type": "Value"
234
+ },
235
+ "glasses": {
236
+ "dtype": "int32",
237
+ "id": null,
238
+ "_type": "Value"
239
+ },
240
+ "glasses_num_categories": {
241
+ "dtype": "int32",
242
+ "id": null,
243
+ "_type": "Value"
244
+ },
245
+ "glasses_color": {
246
+ "dtype": "int32",
247
+ "id": null,
248
+ "_type": "Value"
249
+ },
250
+ "glasses_color_num_categories": {
251
+ "dtype": "int32",
252
+ "id": null,
253
+ "_type": "Value"
254
+ },
255
+ "eye_slant": {
256
+ "dtype": "int32",
257
+ "id": null,
258
+ "_type": "Value"
259
+ },
260
+ "eye_slant_num_categories": {
261
+ "dtype": "int32",
262
+ "id": null,
263
+ "_type": "Value"
264
+ },
265
+ "eyebrow_width": {
266
+ "dtype": "int32",
267
+ "id": null,
268
+ "_type": "Value"
269
+ },
270
+ "eyebrow_width_num_categories": {
271
+ "dtype": "int32",
272
+ "id": null,
273
+ "_type": "Value"
274
+ },
275
+ "eye_eyebrow_distance": {
276
+ "dtype": "int32",
277
+ "id": null,
278
+ "_type": "Value"
279
+ },
280
+ "eye_eyebrow_distance_num_categories": {
281
+ "dtype": "int32",
282
+ "id": null,
283
+ "_type": "Value"
284
+ }
285
+ },
286
+ "post_processed": null,
287
+ "supervised_keys": {
288
+ "input": "img_bytes",
289
+ "output": ""
290
+ },
291
+ "task_templates": null,
292
+ "builder_name": "cartoonset",
293
+ "config_name": "10k+features",
294
+ "version": {
295
+ "version_str": "1.0.0",
296
+ "description": "",
297
+ "major": 1,
298
+ "minor": 0,
299
+ "patch": 0
300
+ },
301
+ "splits": {
302
+ "train": {
303
+ "name": "train",
304
+ "num_bytes": 489776874,
305
+ "num_examples": 10000,
306
+ "dataset_name": "cartoonset"
307
+ }
308
+ },
309
+ "download_checksums": {
310
+ "https://huggingface.co/datasets/cgarciae/cartoonset/resolve/1.0.0/data/cartoonset10k.tgz": {
311
+ "num_bytes": 476635078,
312
+ "checksum": "902145ad5096af10ca9ee200b648e2a60e8b6b83dd2f4dc5356817988be8ff43"
313
+ }
314
+ },
315
+ "download_size": 476635078,
316
+ "post_processing_size": null,
317
+ "dataset_size": 489776874,
318
+ "size_in_bytes": 966411952
319
+ },
320
+ "100k+features": {
321
+ "description": "Cartoon Set is a collection of random, 2D cartoon avatar images. The cartoons vary in 10 artwork \ncategories, 4 color categories, and 4 proportion categories, with a total of ~1013 possible \ncombinations. We provide sets of 10k and 100k randomly chosen cartoons and labeled attributes. \n",
322
+ "citation": "\n@article{DBLP:journals/corr/abs-1711-05139,\n author = {Amelie Royer and\n Konstantinos Bousmalis and\n Stephan Gouws and\n Fred Bertsch and\n Inbar Mosseri and\n Forrester Cole and\n Kevin Murphy},\n title = {{XGAN:} Unsupervised Image-to-Image Translation for many-to-many Mappings},\n journal = {CoRR},\n volume = {abs/1711.05139},\n year = {2017},\n url = {http://arxiv.org/abs/1711.05139},\n eprinttype = {arXiv},\n eprint = {1711.05139},\n timestamp = {Mon, 13 Aug 2018 16:47:38 +0200},\n biburl = {https://dblp.org/rec/journals/corr/abs-1711-05139.bib},\n bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n",
323
+ "homepage": "https://www.cs.toronto.edu/~kriz/cifar.html",
324
+ "license": "",
325
+ "features": {
326
+ "img_bytes": {
327
+ "dtype": "binary",
328
+ "id": null,
329
+ "_type": "Value"
330
+ },
331
+ "eye_angle": {
332
+ "dtype": "int32",
333
+ "id": null,
334
+ "_type": "Value"
335
+ },
336
+ "eye_angle_num_categories": {
337
+ "dtype": "int32",
338
+ "id": null,
339
+ "_type": "Value"
340
+ },
341
+ "eye_lashes": {
342
+ "dtype": "int32",
343
+ "id": null,
344
+ "_type": "Value"
345
+ },
346
+ "eye_lashes_num_categories": {
347
+ "dtype": "int32",
348
+ "id": null,
349
+ "_type": "Value"
350
+ },
351
+ "eye_lid": {
352
+ "dtype": "int32",
353
+ "id": null,
354
+ "_type": "Value"
355
+ },
356
+ "eye_lid_num_categories": {
357
+ "dtype": "int32",
358
+ "id": null,
359
+ "_type": "Value"
360
+ },
361
+ "chin_length": {
362
+ "dtype": "int32",
363
+ "id": null,
364
+ "_type": "Value"
365
+ },
366
+ "chin_length_num_categories": {
367
+ "dtype": "int32",
368
+ "id": null,
369
+ "_type": "Value"
370
+ },
371
+ "eyebrow_weight": {
372
+ "dtype": "int32",
373
+ "id": null,
374
+ "_type": "Value"
375
+ },
376
+ "eyebrow_weight_num_categories": {
377
+ "dtype": "int32",
378
+ "id": null,
379
+ "_type": "Value"
380
+ },
381
+ "eyebrow_shape": {
382
+ "dtype": "int32",
383
+ "id": null,
384
+ "_type": "Value"
385
+ },
386
+ "eyebrow_shape_num_categories": {
387
+ "dtype": "int32",
388
+ "id": null,
389
+ "_type": "Value"
390
+ },
391
+ "eyebrow_thickness": {
392
+ "dtype": "int32",
393
+ "id": null,
394
+ "_type": "Value"
395
+ },
396
+ "eyebrow_thickness_num_categories": {
397
+ "dtype": "int32",
398
+ "id": null,
399
+ "_type": "Value"
400
+ },
401
+ "face_shape": {
402
+ "dtype": "int32",
403
+ "id": null,
404
+ "_type": "Value"
405
+ },
406
+ "face_shape_num_categories": {
407
+ "dtype": "int32",
408
+ "id": null,
409
+ "_type": "Value"
410
+ },
411
+ "facial_hair": {
412
+ "dtype": "int32",
413
+ "id": null,
414
+ "_type": "Value"
415
+ },
416
+ "facial_hair_num_categories": {
417
+ "dtype": "int32",
418
+ "id": null,
419
+ "_type": "Value"
420
+ },
421
+ "hair": {
422
+ "dtype": "int32",
423
+ "id": null,
424
+ "_type": "Value"
425
+ },
426
+ "hair_num_categories": {
427
+ "dtype": "int32",
428
+ "id": null,
429
+ "_type": "Value"
430
+ },
431
+ "eye_color": {
432
+ "dtype": "int32",
433
+ "id": null,
434
+ "_type": "Value"
435
+ },
436
+ "eye_color_num_categories": {
437
+ "dtype": "int32",
438
+ "id": null,
439
+ "_type": "Value"
440
+ },
441
+ "face_color": {
442
+ "dtype": "int32",
443
+ "id": null,
444
+ "_type": "Value"
445
+ },
446
+ "face_color_num_categories": {
447
+ "dtype": "int32",
448
+ "id": null,
449
+ "_type": "Value"
450
+ },
451
+ "hair_color": {
452
+ "dtype": "int32",
453
+ "id": null,
454
+ "_type": "Value"
455
+ },
456
+ "hair_color_num_categories": {
457
+ "dtype": "int32",
458
+ "id": null,
459
+ "_type": "Value"
460
+ },
461
+ "glasses": {
462
+ "dtype": "int32",
463
+ "id": null,
464
+ "_type": "Value"
465
+ },
466
+ "glasses_num_categories": {
467
+ "dtype": "int32",
468
+ "id": null,
469
+ "_type": "Value"
470
+ },
471
+ "glasses_color": {
472
+ "dtype": "int32",
473
+ "id": null,
474
+ "_type": "Value"
475
+ },
476
+ "glasses_color_num_categories": {
477
+ "dtype": "int32",
478
+ "id": null,
479
+ "_type": "Value"
480
+ },
481
+ "eye_slant": {
482
+ "dtype": "int32",
483
+ "id": null,
484
+ "_type": "Value"
485
+ },
486
+ "eye_slant_num_categories": {
487
+ "dtype": "int32",
488
+ "id": null,
489
+ "_type": "Value"
490
+ },
491
+ "eyebrow_width": {
492
+ "dtype": "int32",
493
+ "id": null,
494
+ "_type": "Value"
495
+ },
496
+ "eyebrow_width_num_categories": {
497
+ "dtype": "int32",
498
+ "id": null,
499
+ "_type": "Value"
500
+ },
501
+ "eye_eyebrow_distance": {
502
+ "dtype": "int32",
503
+ "id": null,
504
+ "_type": "Value"
505
+ },
506
+ "eye_eyebrow_distance_num_categories": {
507
+ "dtype": "int32",
508
+ "id": null,
509
+ "_type": "Value"
510
+ }
511
+ },
512
+ "post_processed": null,
513
+ "supervised_keys": {
514
+ "input": "img_bytes",
515
+ "output": ""
516
+ },
517
+ "task_templates": null,
518
+ "builder_name": "cartoonset",
519
+ "config_name": "100k+features",
520
+ "version": {
521
+ "version_str": "1.0.0",
522
+ "description": "",
523
+ "major": 1,
524
+ "minor": 0,
525
+ "patch": 0
526
+ },
527
+ "splits": {
528
+ "train": {
529
+ "name": "train",
530
+ "num_bytes": 4904016989,
531
+ "num_examples": 100000,
532
+ "dataset_name": "cartoonset"
533
+ }
534
+ },
535
+ "download_checksums": {
536
+ "https://huggingface.co/datasets/cgarciae/cartoonset/resolve/1.0.0/data/cartoonset100k.tgz": {
537
+ "num_bytes": 4766442036,
538
+ "checksum": "541dffe1016b10e8177dd534188fa729c8ed9111734498c405d55e47aae1a2a6"
539
+ }
540
+ },
541
+ "download_size": 4766442036,
542
+ "post_processing_size": null,
543
+ "dataset_size": 4904016989,
544
+ "size_in_bytes": 9670459025
545
  }
546
  }