Convert dataset to Parquet

README.md

@@ -36,13 +36,21 @@ dataset_info:
           '25': Z
   splits:
   - name: train
-    num_bytes: 52628800
+    num_bytes: 52114000.0
     num_examples: 124800
   - name: test
-    num_bytes: 8775753
+    num_bytes: 8689953.0
     num_examples: 20800
-  download_size: 36381774
-  dataset_size: 61404553
+  download_size: 58971617
+  dataset_size: 60803953.0
+configs:
+- config_name: emnist-letters
+  data_files:
+  - split: train
+    path: emnist-letters/train-*
+  - split: test
+    path: emnist-letters/test-*
+  default: true
 ---
 
 # Dataset Card for "emnist-letters"

emnist-letters-mapping.txt

@@ -1,26 +0,0 @@
-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

raw/emnist-letters-train-labels-idx1-ubyte.gz → emnist-letters/test-00000-of-00001.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:a2e54aeb01b0583309766385d163418cdcefafa9a5f76dfa4953b41b52cbe40e
-size 79283
+oid sha256:76421442e28d4ba81c10016df2713d18105df34169f405e0eb43470e88b5d98e
+size 8407069

raw/emnist-letters-train-images-idx3-ubyte.gz → emnist-letters/train-00000-of-00001.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:58aec6425acf8636ad64ecec86a1514a0519e08d10fa7054d387862b44845d6c
-size 31197176
+oid sha256:756ccd59b40c47fd5a7fc622ed1c34d32576d64edc37aaf9accc89de2bf0bb35
+size 50564548

emnist_letters.py

@@ -1,89 +0,0 @@
-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

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:201c0a59fa093429287ae63a69d551fc68e07848e076d7c75b14e99d21985c4f
-size 5105171

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

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:6c27a54187ec374e126764d8a67d8db4584829e07dcec40ed9da015112fb3866
-size 144