add usage section

README.md

@@ -12,25 +12,13 @@ license: cc-by-4.0
   - [Table of Contents](#table-of-contents)
   - [Dataset Description](#dataset-description)
     - [Dataset Summary](#dataset-summary)
-    - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
-    - [Languages](#languages)
+      - [Usage](#usage)
   - [Dataset Structure](#dataset-structure)
     - [Data Instances](#data-instances)
     - [Data Fields](#data-fields)
     - [Data Splits](#data-splits)
   - [Dataset Creation](#dataset-creation)
     - [Curation Rationale](#curation-rationale)
-    - [Source Data](#source-data)
-      - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
-      - [Who are the source language producers?](#who-are-the-source-language-producers)
-    - [Annotations](#annotations)
-      - [Annotation process](#annotation-process)
-      - [Who are the annotators?](#who-are-the-annotators)
-    - [Personal and Sensitive Information](#personal-and-sensitive-information)
-  - [Considerations for Using the Data](#considerations-for-using-the-data)
-    - [Social Impact of Dataset](#social-impact-of-dataset)
-    - [Discussion of Biases](#discussion-of-biases)
-    - [Other Known Limitations](#other-known-limitations)
   - [Additional Information](#additional-information)
     - [Dataset Curators](#dataset-curators)
     - [Licensing Information](#licensing-information)
@@ -38,7 +26,7 @@ license: cc-by-4.0
     - [Contributions](#contributions)
 ## Dataset Description
 - **Homepage:** https://google.github.io/cartoonset/
-- **Repository:** 
+- **Repository:** https://github.com/google/cartoonset/
 - **Paper:** XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings
 - **Leaderboard:**
 - **Point of Contact:**
@@ -46,58 +34,64 @@ license: cc-by-4.0
 
 ![Cartoon Set sample image](https://huggingface.co/datasets/cgarciae/cartoonset/resolve/main/sample.png)
 
-Cartoon Set 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 ~1013 possible combinations. We provide sets of 10k and 100k randomly chosen cartoons and labeled attributes. 
+[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 ~1013 possible combinations. We provide sets of 10k and 100k randomly chosen cartoons and labeled attributes.
+
+#### Usage
+`cartoonset` provides the images as 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=channels)
+    ...
+    return {"img": img}
+
+ds = ds.map(process_fn)
+```
 
-### Supported Tasks and Leaderboards
-- `image-classification`: The goal of this task is to classify a given image into one of 10 classes. The leaderboard is available [here](https://paperswithcode.com/sota/image-classification-on-Cartoon Set).
-### Languages
-English
 ## Dataset Structure
 ### Data Instances
 A sample from the training set is provided below:
 ```
 {
-  'img': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=32x32 at 0x201FA6EE748>,
-  'label': 0
+  'img_bytes': b'0x...',
 }
 ```
 ### Data Fields
-- img: A `PIL.Image.Image` object containing the 32x32 image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
-- label: 0-9 with the following correspondence
-         0 airplane
-         1 automobile
-         2 bird
-         3 cat
-         4 deer
-         5 dog
-         6 frog
-         7 horse
-         8 ship
-         9 truck
+- img_bytes: A byte string containing the raw data of a 500x500 PNG image.
 ### Data Splits
-Train and Test
+Train
 ## Dataset Creation
 ### Curation Rationale
 [More Information Needed]
-### Source Data
-#### Initial Data Collection and Normalization
-[More Information Needed]
-#### Who are the source language producers?
-[More Information Needed]
-### Annotations
-#### Annotation process
-[More Information Needed]
-#### Who are the annotators?
-[More Information Needed]
-### Personal and Sensitive Information
-[More Information Needed]
-## Considerations for Using the Data
-### Social Impact of Dataset
-[More Information Needed]
-### Discussion of Biases
-[More Information Needed]
-### Other Known Limitations
-[More Information Needed]
 ## Additional Information
 ### Dataset Curators
 [More Information Needed]
@@ -105,13 +99,24 @@ Train and Test
 [More Information Needed]
 ### Citation Information
 ```
-@TECHREPORT{Krizhevsky09learningmultiple,
-    author = {Alex Krizhevsky},
-    title = {Learning multiple layers of features from tiny images},
-    institution = {},
-    year = {2009}
+@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
-
-Thanks to [@czabo](https://github.com/czabo) for adding this dataset.