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README.md

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+---
+dataset_info:
+  config_name: emnist-letters
+  features:
+  - name: image
+    dtype: image
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': A
+          '1': B
+          '2': C
+          '3': D
+          '4': E
+          '5': F
+          '6': G
+          '7': H
+          '8': I
+          '9': J
+          '10': K
+          '11': L
+          '12': M
+          '13': N
+          '14': O
+          '15': P
+          '16': Q
+          '17': R
+          '18': S
+          '19': T
+          '20': U
+          '21': V
+          '22': W
+          '23': X
+          '24': Y
+          '25': Z
+  splits:
+  - name: train
+    num_bytes: 52628800
+    num_examples: 124800
+  - name: test
+    num_bytes: 8775753
+    num_examples: 20800
+  download_size: 36381774
+  dataset_size: 61404553
+---
+
+# Dataset Card for "emnist-letters"
+
+## Dataset Information
+
+The `emnist-letters` dataset is a set of images of handwritten letters. 
+The dataset is split into a training set and a test set.
+
+## Data Fields
+
+- `image`: The image of the handwritten letter. The data type of this field is `image`.
+- `label`: The label of the handwritten letter. The data type of this field is `class_label`, and it can take on the values 'A' to 'Z'.
+
+## Data Splits
+
+- `train`: The training set consists of 124800 examples, with a total size of 52628800 bytes.
+- `test`: The test set consists of 20800 examples, with a total size of 8775753 bytes.

emnist-letters-mapping.txt

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+1 65 97
+2 66 98
+3 67 99
+4 68 100
+5 69 101
+6 70 102
+7 71 103
+8 72 104
+9 73 105
+10 74 106
+11 75 107
+12 76 108
+13 77 109
+14 78 110
+15 79 111
+16 80 112
+17 81 113
+18 82 114
+19 83 115
+20 84 116
+21 85 117
+22 86 118
+23 87 119
+24 88 120
+25 89 121
+26 90 122

emnist_letters.py

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+import struct
+
+import numpy as np
+
+import datasets
+from datasets.tasks import ImageClassification
+
+
+_URL = "./raw/"
+_URLS = {
+    "train_images": "emnist-letters-train-images-idx3-ubyte.gz",
+    "train_labels": "emnist-letters-train-labels-idx1-ubyte.gz",
+    "test_images": "emnist-letters-test-images-idx3-ubyte.gz",
+    "test_labels": "emnist-letters-test-labels-idx1-ubyte.gz",
+}
+
+
+class EMNIST(datasets.GeneratorBasedBuilder):
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="emnist-letters",
+            version=datasets.Version("1.0.0"),
+        )
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            features=datasets.Features(
+                {
+                    "image": datasets.Image(),
+                    "label": datasets.features.ClassLabel(
+                        names=list(chr(i) for i in range(65, 91))
+                    ),
+                }
+            ),
+            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) - 1
+
+        for idx in range(size):
+            yield idx, {"image": images[idx], "label": str(labels[idx])}

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

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+oid sha256:201c0a59fa093429287ae63a69d551fc68e07848e076d7c75b14e99d21985c4f
+size 5105171

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

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+oid sha256:6c27a54187ec374e126764d8a67d8db4584829e07dcec40ed9da015112fb3866
+size 144

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

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

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