Create kmnist.py

kmnist.py

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+import numpy as np
+import datasets
+from PIL import Image
+
+
+class KMNIST(datasets.GeneratorBasedBuilder):
+    """Kuzushiji-MNIST and Kuzushiji-49 datasets."""
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="kmnist", description="Kuzushiji-MNIST dataset with 10 classes."),
+        datasets.BuilderConfig(name="k49mnist", description="Kuzushiji-49 dataset with 49 classes."),
+    ]
+
+    def _info(self):
+        if self.config.name == "kmnist":
+            num_classes = 10
+        else:
+            num_classes = 49
+        return datasets.DatasetInfo(
+            description="Kuzushiji-MNIST and Kuzushiji-49 datasets.",
+            features=datasets.Features({
+                "image": datasets.Image(),  # Automatically converts to PIL.Image
+                "label": datasets.ClassLabel(num_classes=num_classes),
+            }),
+            supervised_keys=("image", "label"),
+            license="CC BY-SA 4.0",
+            homepage="https://github.com/rois-codh/kmnist",
+            citation="""
+                @online{clanuwat2018deep,
+                  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},
+                  date         = {2018-12-03},
+                  year         = {2018},
+                  eprintclass  = {cs.CV},
+                  eprinttype   = {arXiv},
+                  eprint       = {cs.CV/1812.01718},
+                }
+            """
+        )
+
+    def _split_generators(self, dl_manager):
+        urls = {
+            "kmnist": {
+                "train_imgs": "http://codh.rois.ac.jp/kmnist/dataset/kmnist/kmnist-train-imgs.npz",
+                "train_labels": "http://codh.rois.ac.jp/kmnist/dataset/kmnist/kmnist-train-labels.npz",
+                "test_imgs": "http://codh.rois.ac.jp/kmnist/dataset/kmnist/kmnist-test-imgs.npz",
+                "test_labels": "http://codh.rois.ac.jp/kmnist/dataset/kmnist/kmnist-test-labels.npz",
+            },
+            "k49mnist": {
+                "train_imgs": "http://codh.rois.ac.jp/kmnist/dataset/k49/k49-train-imgs.npz",
+                "train_labels": "http://codh.rois.ac.jp/kmnist/dataset/k49/k49-train-labels.npz",
+                "test_imgs": "http://codh.rois.ac.jp/kmnist/dataset/k49/k49-test-imgs.npz",
+                "test_labels": "http://codh.rois.ac.jp/kmnist/dataset/k49/k49-test-labels.npz",
+            },
+        }
+        selected_urls = urls[self.config.name]
+        downloaded_files = dl_manager.download(selected_urls)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "images_path": downloaded_files["train_imgs"],
+                    "labels_path": downloaded_files["train_labels"]
+                }
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "images_path": downloaded_files["test_imgs"],
+                    "labels_path": downloaded_files["test_labels"]
+                }
+            ),
+        ]
+
+    def _generate_examples(self, images_path, labels_path):
+        images = np.load(images_path)["arr_0"]
+        labels = np.load(labels_path)["arr_0"]
+
+        for idx, (image, label) in enumerate(zip(images, labels)):
+            # Convert each image to a PIL.Image object
+            image = Image.fromarray(image, mode="L")  # Mode "L" for grayscale images
+            yield idx, {"image": image, "label": int(label)}