Fix task tags

README.md

@@ -1,187 +1,191 @@
----
-pretty_name: Cartoon Set
-size_categories:
-- 10K<n<100K
-task_categories:
-- image
-- computer-vision
-- generative-modelling
-license: cc-by-4.0
----
-# Dataset Card for Cartoon Set
-## Table of Contents
-- [Dataset Card for Cartoon Set](#dataset-card-for-cartoon-set)
-  - [Table of Contents](#table-of-contents)
-  - [Dataset Description](#dataset-description)
-    - [Dataset Summary](#dataset-summary)
-      - [Usage](#usage)
-  - [Dataset Structure](#dataset-structure)
-    - [Data Instances](#data-instances)
-    - [Data Fields](#data-fields)
-    - [Data Splits](#data-splits)
-  - [Dataset Creation](#dataset-creation)
-    - [Licensing Information](#licensing-information)
-    - [Citation Information](#citation-information)
-    - [Contributions](#contributions)
-## Dataset Description
-- **Homepage:** https://google.github.io/cartoonset/
-- **Repository:** https://github.com/google/cartoonset/
-- **Paper:** XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings
-- **Leaderboard:**
-- **Point of Contact:**
-### Dataset Summary
-
-![Cartoon Set sample image](https://huggingface.co/datasets/cgarciae/cartoonset/resolve/main/sample.png)
-
-[Cartoon Set](https://google.github.io/cartoonset/) is a collection of random, 2D cartoon avatar images. The cartoons vary in 10 artwork categories, 4 color categories, and 4 proportion categories, with a total of ~10^13 possible combinations. We provide sets of 10k and 100k randomly chosen cartoons and labeled attributes.
-
-#### Usage
-`cartoonset` provides the images as PNG byte strings, this gives you a bit more flexibility into how to load the data. Here we show 2 ways:
-
-**Using PIL:**
-```python
-import datasets
-from io import BytesIO
-from PIL import Image
-
-ds = datasets.load_dataset("cgarciae/cartoonset", "10k") # or "100k"
-
-def process_fn(sample):
-    img = Image.open(BytesIO(sample["img_bytes"]))
-    ...
-    return {"img": img}
-
-ds = ds.map(process_fn, remove_columns=["img_bytes"])
-```
-
-**Using TensorFlow:**
-```python
-import datasets
-import tensorflow as tf
-
-hfds = datasets.load_dataset("cgarciae/cartoonset", "10k") # or "100k"
-
-ds = tf.data.Dataset.from_generator(
-    lambda: hfds,
-    output_signature={
-        "img_bytes": tf.TensorSpec(shape=(), dtype=tf.string),
-    },
-)
-
-def process_fn(sample):
-    img = tf.image.decode_png(sample["img_bytes"], channels=3)
-    ...
-    return {"img": img}
-
-ds = ds.map(process_fn)
-```
-
-**Additional features:**
-You can also access the features that generated each sample e.g:
-
-```python
-ds = datasets.load_dataset("cgarciae/cartoonset", "10k+features") # or "100k+features"
-```
-
-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. See [Data Fields](#data-fields) for the complete list of features.
-
-## Dataset Structure
-### Data Instances
-A sample from the training set is provided below:
-```python
-{
-  'img_bytes': b'0x...',
-}
-```
-If `+features` is added to the dataset name, the following additional fields are provided:
-
-```python
-{
-  'img_bytes': b'0x...',
-  'eye_angle': 0,
-  'eye_angle_num_categories': 3,
-  'eye_lashes': 0,
-  'eye_lashes_num_categories': 2,
-  'eye_lid': 0,
-  'eye_lid_num_categories': 2,
-  'chin_length': 2,
-  'chin_length_num_categories': 3,
-  ...
-}
-```
-
-### Data Fields
-- `img_bytes`: A byte string containing the raw data of a 500x500 PNG image.
-
-If `+features` is appended to the dataset name, the following additional `int32` fields are provided:
-
-- `eye_angle`
-- `eye_angle_num_categories`
-- `eye_lashes`
-- `eye_lashes_num_categories`
-- `eye_lid`
-- `eye_lid_num_categories`
-- `chin_length`
-- `chin_length_num_categories`
-- `eyebrow_weight`
-- `eyebrow_weight_num_categories`
-- `eyebrow_shape`
-- `eyebrow_shape_num_categories`
-- `eyebrow_thickness`
-- `eyebrow_thickness_num_categories`
-- `face_shape`
-- `face_shape_num_categories`
-- `facial_hair`
-- `facial_hair_num_categories`
-- `facial_hair_num_categories`
-- `facial_hair_num_categories`
-- `hair`
-- `hair_num_categories`
-- `hair_num_categories`
-- `hair_num_categories`
-- `eye_color`
-- `eye_color_num_categories`
-- `face_color`
-- `face_color_num_categories`
-- `hair_color`
-- `hair_color_num_categories`
-- `glasses`
-- `glasses_num_categories`
-- `glasses_color`
-- `glasses_color_num_categories`
-- `eyes_slant`
-- `eye_slant_num_categories`
-- `eyebrow_width`
-- `eyebrow_width_num_categories`
-- `eye_eyebrow_distance`
-- `eye_eyebrow_distance_num_categories`
-
-
-### Data Splits
-Train
-## Dataset Creation
-### Licensing Information
-This data is licensed by Google LLC under a Creative Commons Attribution 4.0 International License.
-### Citation Information
-```
-@article{DBLP:journals/corr/abs-1711-05139,
-  author    = {Amelie Royer and
-               Konstantinos Bousmalis and
-               Stephan Gouws and
-               Fred Bertsch and
-               Inbar Mosseri and
-               Forrester Cole and
-               Kevin Murphy},
-  title     = {{XGAN:} Unsupervised Image-to-Image Translation for many-to-many Mappings},
-  journal   = {CoRR},
-  volume    = {abs/1711.05139},
-  year      = {2017},
-  url       = {http://arxiv.org/abs/1711.05139},
-  eprinttype = {arXiv},
-  eprint    = {1711.05139},
-  timestamp = {Mon, 13 Aug 2018 16:47:38 +0200},
-  biburl    = {https://dblp.org/rec/journals/corr/abs-1711-05139.bib},
-  bibsource = {dblp computer science bibliography, https://dblp.org}
-}
-```
-### Contributions
+---
+license:
+- cc-by-4.0
+size_categories:
+- 10K<n<100K
+task_categories:
+- other
+tags:
+- image
+- computer-vision
+- generative-modeling
+pretty_name: Cartoon Set
+---
+
+# Dataset Card for Cartoon Set
+## Table of Contents
+- [Dataset Card for Cartoon Set](#dataset-card-for-cartoon-set)
+  - [Table of Contents](#table-of-contents)
+  - [Dataset Description](#dataset-description)
+    - [Dataset Summary](#dataset-summary)
+      - [Usage](#usage)
+  - [Dataset Structure](#dataset-structure)
+    - [Data Instances](#data-instances)
+    - [Data Fields](#data-fields)
+    - [Data Splits](#data-splits)
+  - [Dataset Creation](#dataset-creation)
+    - [Licensing Information](#licensing-information)
+    - [Citation Information](#citation-information)
+    - [Contributions](#contributions)
+## Dataset Description
+- **Homepage:** https://google.github.io/cartoonset/
+- **Repository:** https://github.com/google/cartoonset/
+- **Paper:** XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings
+- **Leaderboard:**
+- **Point of Contact:**
+### Dataset Summary
+
+![Cartoon Set sample image](https://huggingface.co/datasets/cgarciae/cartoonset/resolve/main/sample.png)
+
+[Cartoon Set](https://google.github.io/cartoonset/) is a collection of random, 2D cartoon avatar images. The cartoons vary in 10 artwork categories, 4 color categories, and 4 proportion categories, with a total of ~10^13 possible combinations. We provide sets of 10k and 100k randomly chosen cartoons and labeled attributes.
+
+#### Usage
+`cartoonset` provides the images as PNG byte strings, this gives you a bit more flexibility into how to load the data. Here we show 2 ways:
+
+**Using PIL:**
+```python
+import datasets
+from io import BytesIO
+from PIL import Image
+
+ds = datasets.load_dataset("cgarciae/cartoonset", "10k") # or "100k"
+
+def process_fn(sample):
+    img = Image.open(BytesIO(sample["img_bytes"]))
+    ...
+    return {"img": img}
+
+ds = ds.map(process_fn, remove_columns=["img_bytes"])
+```
+
+**Using TensorFlow:**
+```python
+import datasets
+import tensorflow as tf
+
+hfds = datasets.load_dataset("cgarciae/cartoonset", "10k") # or "100k"
+
+ds = tf.data.Dataset.from_generator(
+    lambda: hfds,
+    output_signature={
+        "img_bytes": tf.TensorSpec(shape=(), dtype=tf.string),
+    },
+)
+
+def process_fn(sample):
+    img = tf.image.decode_png(sample["img_bytes"], channels=3)
+    ...
+    return {"img": img}
+
+ds = ds.map(process_fn)
+```
+
+**Additional features:**
+You can also access the features that generated each sample e.g:
+
+```python
+ds = datasets.load_dataset("cgarciae/cartoonset", "10k+features") # or "100k+features"
+```
+
+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. See [Data Fields](#data-fields) for the complete list of features.
+
+## Dataset Structure
+### Data Instances
+A sample from the training set is provided below:
+```python
+{
+  'img_bytes': b'0x...',
+}
+```
+If `+features` is added to the dataset name, the following additional fields are provided:
+
+```python
+{
+  'img_bytes': b'0x...',
+  'eye_angle': 0,
+  'eye_angle_num_categories': 3,
+  'eye_lashes': 0,
+  'eye_lashes_num_categories': 2,
+  'eye_lid': 0,
+  'eye_lid_num_categories': 2,
+  'chin_length': 2,
+  'chin_length_num_categories': 3,
+  ...
+}
+```
+
+### Data Fields
+- `img_bytes`: A byte string containing the raw data of a 500x500 PNG image.
+
+If `+features` is appended to the dataset name, the following additional `int32` fields are provided:
+
+- `eye_angle`
+- `eye_angle_num_categories`
+- `eye_lashes`
+- `eye_lashes_num_categories`
+- `eye_lid`
+- `eye_lid_num_categories`
+- `chin_length`
+- `chin_length_num_categories`
+- `eyebrow_weight`
+- `eyebrow_weight_num_categories`
+- `eyebrow_shape`
+- `eyebrow_shape_num_categories`
+- `eyebrow_thickness`
+- `eyebrow_thickness_num_categories`
+- `face_shape`
+- `face_shape_num_categories`
+- `facial_hair`
+- `facial_hair_num_categories`
+- `facial_hair_num_categories`
+- `facial_hair_num_categories`
+- `hair`
+- `hair_num_categories`
+- `hair_num_categories`
+- `hair_num_categories`
+- `eye_color`
+- `eye_color_num_categories`
+- `face_color`
+- `face_color_num_categories`
+- `hair_color`
+- `hair_color_num_categories`
+- `glasses`
+- `glasses_num_categories`
+- `glasses_color`
+- `glasses_color_num_categories`
+- `eyes_slant`
+- `eye_slant_num_categories`
+- `eyebrow_width`
+- `eyebrow_width_num_categories`
+- `eye_eyebrow_distance`
+- `eye_eyebrow_distance_num_categories`
+
+
+### Data Splits
+Train
+## Dataset Creation
+### Licensing Information
+This data is licensed by Google LLC under a Creative Commons Attribution 4.0 International License.
+### Citation Information
+```
+@article{DBLP:journals/corr/abs-1711-05139,
+  author    = {Amelie Royer and
+               Konstantinos Bousmalis and
+               Stephan Gouws and
+               Fred Bertsch and
+               Inbar Mosseri and
+               Forrester Cole and
+               Kevin Murphy},
+  title     = {{XGAN:} Unsupervised Image-to-Image Translation for many-to-many Mappings},
+  journal   = {CoRR},
+  volume    = {abs/1711.05139},
+  year      = {2017},
+  url       = {http://arxiv.org/abs/1711.05139},
+  eprinttype = {arXiv},
+  eprint    = {1711.05139},
+  timestamp = {Mon, 13 Aug 2018 16:47:38 +0200},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-1711-05139.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+```
+### Contributions