feat: yay first dataset

README.md

@@ -1,3 +1,187 @@
 ---
-license: unknown
+annotations_creators:
+- crowdsourced
+language_creators:
+- crowdsourced
+language:
+- en
+license:
+- unknown
+multilinguality:
+- monolingual
+pretty_name: Individuality Of Handwriting
+size_categories:
+- 1K<n<10K
+task_categories:
+- image-classification
+dataset_info:
+  features:
+  - name: image
+    dtype: image
+  - name: label
+    dtype:
+      class_label:
+        names:
+          0: original
+          1: forgeries
+  - name: individual
+    dtype: uint8
+  - name: figure
+    dtype: uint8
+  splits:
+  - name: train
+    num_examples: 1322
+  - name: validation
+    num_examples: 0
 ---
+
+# Dataset Card for Individuality Of Handwriting
+
+## Table of Contents
+- [Dataset Card for Individuality Of Handwriting](#dataset-card-for-individuality-of-handwriting)
+  - [Table of Contents](#table-of-contents)
+  - [Dataset Description](#dataset-description)
+    - [Dataset Summary](#dataset-summary)
+    - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+    - [Languages](#languages)
+  - [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)
+    - [Citation Information](#citation-information)
+    - [Contributions](#contributions)
+
+## Dataset Description
+
+### Dataset Summary
+
+[More Information Needed]
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+English
+
+## Dataset Structure
+
+### Data Instances
+
+A sample from the training set is provided below:
+
+```
+{
+  'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=384x512 at 0x276021C5EB8>,
+  'label': 0,
+  'individual': 10,
+  'figure': 2
+}
+```
+
+### Data Fields
+
+The data instances have the following fields:
+
+- `image`: A `PIL.Image.Image` object containing the 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`: an `int` classification label.
+
+<details>
+  <summary>Class Label Mappings</summary>
+
+  ```json
+  {
+    "original": 0,
+    "forgeries": 1,
+  }
+  ```
+</details>
+
+
+### Data Splits
+
+ 
+|               | train | validation |
+| ------------- | ----: | ---------: |
+| # of examples |  1322 |          0 |
+| percentage    |  100% |         0% |
+
+
+## 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]
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+[More Information Needed]
+
+### Contributions
+
+[More Information Needed]

individuality_of_handwriting.py

@@ -0,0 +1,65 @@
+"""Dataset class for Individuality Of Handwriting dataset."""
+
+import pathlib
+
+from datasets.tasks import ImageClassification
+
+import datasets
+
+_BASE_URL = "https://cedar.buffalo.edu/NIJ/data/signatures.rar"
+
+_HOMEPAGE = "https://cedar.buffalo.edu/NIJ/projectinfo.html"
+
+_DESCRIPTION = """
+This dataset consists of handwriting samples of 1500 individuals, representative of the US population with respect to gender, age, ethnic groups...
+"""
+
+_NAMES = [
+    "original",
+    "forgeries",
+]
+
+class IndividualityOfHandwriting(datasets.GeneratorBasedBuilder):
+    """Food-101 Images dataset."""
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "image": datasets.Image(),
+                    "label": datasets.ClassLabel(names=_NAMES),
+                    "individual": datasets.Value("uint8"),
+                    "figure": datasets.Value("uint8"),
+                }
+            ),
+            supervised_keys=("image", "label"),
+            homepage=_HOMEPAGE,
+            task_templates=[ImageClassification(image_column="image", label_column="label")],
+        )
+
+    def _split_generators(self, dl_manager):
+        archive_path = dl_manager.download_and_extract(_BASE_URL)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "data_dir": archive_path,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, data_dir):
+        """Generate images and labels for splits."""
+        rglob = pathlib.Path(data_dir).rglob("*.png")
+        for index, filepath in enumerate(rglob):
+            filename = filepath.with_suffix("").name
+            label, individual, figure = filename.split("_")
+            
+            yield index, {
+                "image": str(filepath),
+                "label": label,
+                "individual": individual,
+                "figure": figure,
+            }