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README.md

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+---
+dataset_info:
+  config_name: emnist-mnist
+  features:
+  - name: image
+    dtype: image
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': '0'
+          '1': '1'
+          '2': '2'
+          '3': '3'
+          '4': '4'
+          '5': '5'
+          '6': '6'
+          '7': '7'
+          '8': '8'
+          '9': '9'
+  splits:
+  - name: train
+    num_bytes: 25579752
+    num_examples: 60000
+  - name: test
+    num_bytes: 4271749
+    num_examples: 10000
+  download_size: 17594390
+  dataset_size: 29851501
+---
+
+
+# Dataset Card for "emnist-mnist"
+
+## Dataset Information
+
+The `emnist-mnist` dataset is a set of images of handwritten digits. The dataset is split into a training set and a test set.
+
+## Data Fields
+
+- `image`: The image of the handwritten digit. The data type of this field is `image`.
+- `label`: The label of the handwritten digit. The data type of this field is `class_label`, and it can take on the values '0' to '9'.
+
+## Data Splits
+
+- `train`: The training set consists of 60000 examples, with a total size of 25579752 bytes.
+- `test`: The test set consists of 10000 examples, with a total size of 4271749 bytes.

emnist_mnist.py

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+import struct
+
+import numpy as np
+
+import datasets
+from datasets.tasks import ImageClassification
+
+
+_URL = "./raw/"
+_URLS = {
+    "train_images": "emnist-mnist-train-images-idx3-ubyte.gz",
+    "train_labels": "emnist-mnist-train-labels-idx1-ubyte.gz",
+    "test_images": "emnist-mnist-test-images-idx3-ubyte.gz",
+    "test_labels": "emnist-mnist-test-labels-idx1-ubyte.gz",
+}
+
+
+class EMNIST(datasets.GeneratorBasedBuilder):
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="emnist-mnist",
+            version=datasets.Version("1.0.0"),
+        )
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            features=datasets.Features(
+                {
+                    "image": datasets.Image(),
+                    "label": datasets.features.ClassLabel(
+                        names=["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"]
+                    ),
+                }
+            ),
+            supervised_keys=("image", "label"),
+            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])}

raw/emnist-mnist-test-images-idx3-ubyte.gz

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+size 2513615

raw/emnist-mnist-test-labels-idx1-ubyte.gz

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+size 5144

raw/emnist-mnist-train-images-idx3-ubyte.gz

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raw/emnist-mnist-train-labels-idx1-ubyte.gz

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