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README.md

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+---
+dataset_info:
+  config_name: kmnist
+  features:
+  - name: image
+    dtype: image
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': お
+          '1': き
+          '2': す
+          '3': つ
+          '4': な
+          '5': は
+          '6': ま
+          '7': や
+          '8': れ
+          '9': を
+  splits:
+  - name: train
+    num_bytes: 27055217
+    num_examples: 60000
+  - name: test
+    num_bytes: 4520213
+    num_examples: 10000
+  download_size: 21240888
+  dataset_size: 31575430
+---
+
+# KMNIST Dataset
+
+lassify images from the KMNIST dataset into one of the 10 classes, representing different Japanese characters.

kmnist.py

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+import struct
+
+import numpy as np
+
+import datasets
+from datasets.tasks import ImageClassification
+
+_CITATION = R"""
+@article{DBLP:journals/corr/abs-1812-01718,
+  author       = {Tarin Clanuwat and
+                  Mikel Bober{-}Irizar and
+                  Asanobu Kitamoto and
+                  Alex Lamb and
+                  Kazuaki Yamamoto and
+                  David Ha},
+  title        = {Deep Learning for Classical Japanese Literature},
+  journal      = {CoRR},
+  volume       = {abs/1812.01718},
+  year         = {2018},
+  url          = {http://arxiv.org/abs/1812.01718},
+  eprinttype    = {arXiv},
+  eprint       = {1812.01718},
+  timestamp    = {Thu, 14 Oct 2021 09:15:14 +0200},
+  biburl       = {https://dblp.org/rec/journals/corr/abs-1812-01718.bib},
+  bibsource    = {dblp computer science bibliography, https://dblp.org}
+}
+"""
+
+_URL = "./raw/"
+_URLS = {
+    "train_images": "train-images-idx3-ubyte.gz",
+    "train_labels": "train-labels-idx1-ubyte.gz",
+    "test_images": "t10k-images-idx3-ubyte.gz",
+    "test_labels": "t10k-labels-idx1-ubyte.gz",
+}
+
+
+class KMNIST(datasets.GeneratorBasedBuilder):
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="kmnist",
+            version=datasets.Version("1.0.0"),
+        )
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            features=datasets.Features(
+                {
+                    "image": datasets.Image(),
+                    "label": datasets.features.ClassLabel(
+                        names=[
+                            "お",
+                            "き",
+                            "す",
+                            "つ",
+                            "な",
+                            "は",
+                            "ま",
+                            "や",
+                            "れ",
+                            "を",
+                        ]
+                    ),
+                }
+            ),
+            supervised_keys=("image", "label"),
+            homepage="https://github.com/rois-codh/kmnist",
+            citation=_CITATION,
+            task_templates=[
+                ImageClassification(
+                    image_column="image",
+                    label_column="label",
+                )
+            ],
+        )
+
+    def _split_generators(self, dl_manager):
+        urls_to_download = {key: _URL + fname for key, fname in _URLS.items()}
+        downloaded_files = dl_manager.download_and_extract(urls_to_download)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": (
+                        downloaded_files["train_images"],
+                        downloaded_files["train_labels"],
+                    ),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": (
+                        downloaded_files["test_images"],
+                        downloaded_files["test_labels"],
+                    ),
+                    "split": "test",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        """This function returns the examples in the raw form."""
+        # Images
+        with open(filepath[0], "rb") as f:
+            # First 16 bytes contain some metadata
+            _ = f.read(4)
+            size = struct.unpack(">I", f.read(4))[0]
+            _ = f.read(8)
+            images = np.frombuffer(f.read(), dtype=np.uint8).reshape(size, 28, 28)
+
+        # Labels
+        with open(filepath[1], "rb") as f:
+            # First 8 bytes contain some metadata
+            _ = f.read(8)
+            labels = np.frombuffer(f.read(), dtype=np.uint8)
+
+        for idx in range(size):
+            yield idx, {"image": images[idx], "label": str(labels[idx])}

kmnist_classmap.csv

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+index,codepoint,char
+0,U+304A,お
+1,U+304D,き
+2,U+3059,す
+3,U+3064,つ
+4,U+306A,な
+5,U+306F,は
+6,U+307E,ま
+7,U+3084,や
+8,U+308C,れ
+9,U+3092,を

raw/t10k-images-idx3-ubyte.gz

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+oid sha256:edd7a857845ad6bb1d0ba43fe7e794d164fe2dce499a1694695a792adfac43c5
+size 3041136

raw/t10k-labels-idx1-ubyte.gz

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+oid sha256:20bb9a0ef54c7db3efc55a92eef5582c109615df22683c380526788f98e42a1c
+size 5120

raw/train-images-idx3-ubyte.gz

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+oid sha256:51467d22d8cc72929e2a028a0428f2086b092bb31cfb79c69cc0a90ce135fde4
+size 18165135

raw/train-labels-idx1-ubyte.gz

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+oid sha256:e38f9ebcd0f3ebcdec7fc8eabdcdaef93bb0df8ea12bee65224341c8183d8e17
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