Convert dataset to Parquet (#1)

- Convert dataset to Parquet (680f4e1f20aa57f13050fd56bc5482e7bdf927fd)
- Delete data file (bbd4678281b0e6ccda37b48aa16f72fd9b0a1e71)
- Delete data file (faed3963f1bb093d6e4ad860c0e80339b444efe3)
- Delete data file (bd382ceec3874e96329cf01c6efdf329e9c7083c)
- Delete loading script (1c3320f6684e4ec5a686269410d2b3e51d4f6ad3)
- Delete data file (56ed2e63aee3660fb11b1232160e5984f9979fa6)

README.md

@@ -20,13 +20,21 @@ dataset_info:
           '9': '9'
   splits:
   - name: train
-    num_bytes: 25579752
+    num_bytes: 25332252.0
     num_examples: 60000
   - name: test
-    num_bytes: 4271749
+    num_bytes: 4230499.0
     num_examples: 10000
-  download_size: 17594390
-  dataset_size: 29851501
+  download_size: 28641083
+  dataset_size: 29562751.0
+configs:
+- config_name: emnist-mnist
+  data_files:
+  - split: train
+    path: emnist-mnist/train-*
+  - split: test
+    path: emnist-mnist/test-*
+  default: true
 ---
 
 

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

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:a04b8595692322748140be589a7329918171eb1c7bf91287621e7cf557d20865
-size 29480
+oid sha256:bba0df1f5b08bd2b65609e636c6b0b1c1ea43599801ccc68cf65d145d85ae79a
+size 4102080

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

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:906c3165bfab536456427d8f7d007b7cfa88457cfa88e8ff6fb7bf94c8ccc018
-size 2513615
+oid sha256:6a66b605cb8ceef4769de88d009bee814c631dadf6d20ba6c8ed8dcb3acdb438
+size 24539003

emnist_mnist.py

@@ -1,89 +0,0 @@
-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-labels-idx1-ubyte.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:61b660932af457e9be95b50e11438347f1a4468428ae4efdb21ee65cbffa0031
-size 5144

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

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:ccd6166b6b8702fa8d4929daeaae1722cb5a20f2e5b13ee0c799a3d1294adf85
-size 15046151