Create fashion-mnist.py

fashion-mnist.py

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+import gzip
+import numpy as np
+import datasets
+
+
+class FashionMNIST(datasets.GeneratorBasedBuilder):
+    """Grayscale image classification.
+
+    `Fashion-MNIST` is a dataset of Zalando's article images consisting of a training set of 60,000 examples
+    and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes.
+    """
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description="Fashion-MNIST is a dataset of Zalando's article images for image classification tasks.",
+            features=datasets.Features(
+                {
+                    "image": datasets.Image(),
+                    "label": datasets.ClassLabel(names=[
+                        "T-shirt/top", "Trouser", "Pullover", "Dress", "Coat",
+                        "Sandal", "Shirt", "Sneaker", "Bag", "Ankle boot"
+                    ])
+                }
+            ),
+            supervised_keys=("image", "label"),
+            homepage="https://github.com/zalandoresearch/fashion-mnist",
+            license="MIT License",
+            citation="""@article{xiao2017fashion,
+                         title={Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms},
+                         author={Xiao, Han and Rasul, Kashif and Vollgraf, Roland},
+                         journal={arXiv preprint arXiv:1708.07747},
+                         year={2017}}"""
+        )
+
+    def _split_generators(self, dl_manager):
+        urls = {
+            "train_images": "http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz",
+            "train_labels": "http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz",
+            "test_images": "http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz",
+            "test_labels": "http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz",
+        }
+        downloaded_files = dl_manager.download(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):
+        with gzip.open(images_path, "rb") as img_path:
+            images = np.frombuffer(img_path.read(), dtype=np.uint8, offset=16).reshape(-1, 28, 28)
+        with gzip.open(labels_path, "rb") as lbl_path:
+            labels = np.frombuffer(lbl_path.read(), dtype=np.uint8, offset=8)
+
+        for idx, (image, label) in enumerate(zip(images, labels)):
+            yield idx, {"image": image, "label": label}