Create mnist.py

mnist.py

@@ -0,0 +1,72 @@
+import numpy as np
+import datasets
+import struct
+
+
+class MNIST(datasets.GeneratorBasedBuilder):
+    """MNIST Dataset using raw IDX files for digit classification."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description="""The MNIST database of handwritten digits, with a training set of 60,000 examples, and a test 
+            set of 10,000 examples.""",
+            features=datasets.Features(
+                {
+                    "image": datasets.Image(),
+                    "label": datasets.ClassLabel(num_classes=10),
+                }
+            ),
+            supervised_keys=("image", "label"),
+            homepage="http://yann.lecun.com/exdb/mnist/",
+            citation="""@misc{lecun1998mnist,
+                          author={Yann LeCun and Corinna Cortes and Christopher J.C. Burges},
+                          title={The MNIST database of handwritten digits},
+                          year={1998},
+                          url={http://yann.lecun.com/exdb/mnist/}
+                        }""",
+        )
+
+    def _split_generators(self, dl_manager):
+        # Download IDX files from the specified links
+        urls = {
+            "train_images": "https://storage.googleapis.com/cvdf-datasets/mnist/train-images-idx3-ubyte.gz",
+            "train_labels": "https://storage.googleapis.com/cvdf-datasets/mnist/train-labels-idx1-ubyte.gz",
+            "test_images": "https://storage.googleapis.com/cvdf-datasets/mnist/t10k-images-idx3-ubyte.gz",
+            "test_labels": "https://storage.googleapis.com/cvdf-datasets/mnist/t10k-labels-idx1-ubyte.gz",
+        }
+        downloaded_files = dl_manager.download_and_extract(urls)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "images_path": downloaded_files["train_images"],
+                    "labels_path": downloaded_files["train_labels"],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "images_path": downloaded_files["test_images"],
+                    "labels_path": downloaded_files["test_labels"],
+                },
+            ),
+        ]
+
+    def _generate_examples(self, images_path, labels_path):
+        # Read and parse the decompressed IDX files
+        with open(images_path, "rb") as img_file:
+            _, num_images, rows, cols = struct.unpack(">IIII", img_file.read(16))
+            images = np.frombuffer(img_file.read(), dtype=np.uint8).reshape(num_images, rows, cols)
+
+        with open(labels_path, "rb") as lbl_file:
+            _, num_labels = struct.unpack(">II", lbl_file.read(8))
+            labels = np.frombuffer(lbl_file.read(), dtype=np.uint8)
+
+        assert len(images) == len(labels), "Mismatch between image and label counts."
+
+        for idx, (image, label) in enumerate(zip(images, labels)):
+            # Remove channel dimension for PIL compatibility
+            yield idx, {"image": image, "label": label}