Update files from the datasets library (from 1.17.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.17.0

README.md

@@ -69,12 +69,19 @@ English
 
 ### Data Instances
 
-A data point comprises an image and its label.
+A data point comprises an image and its label:
+
+```
+{
+  'image': <PIL.PngImagePlugin.PngImageFile image mode=L size=28x28 at 0x276021F6DD8>,
+  'label': 5
+}
+```
 
 ### Data Fields
 
-- image: a 2d array of integers representing the 28x28 image.
-- label: an integer between 0 and 9 representing the digit.
+- `image`: A `PIL.Image.Image` object containing the 28x28 image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
+- `label`: an integer between 0 and 9 representing the digit.
 
 ### Data Splits
 

dataset_infos.json

@@ -1 +1 @@
-{"mnist": {"description": "The MNIST dataset consists of 70,000 28x28 black-and-white images in 10 classes (one for each digits), with 7,000\nimages per class. There are 60,000 training images and 10,000 test images.\n", "citation": "@article{lecun2010mnist,\n  title={MNIST handwritten digit database},\n  author={LeCun, Yann and Cortes, Corinna and Burges, CJ},\n  journal={ATT Labs [Online]. Available: http://yann.lecun.com/exdb/mnist},\n  volume={2},\n  year={2010}\n}\n", "homepage": "http://yann.lecun.com/exdb/mnist/", "license": "", "features": {"image": {"shape": [28, 28], "dtype": "uint8", "id": null, "_type": "Array2D"}, "label": {"num_classes": 10, "names": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": {"input": "image", "output": "label"}, "builder_name": "mnist", "config_name": "mnist", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 54480048, "num_examples": 60000, "dataset_name": "mnist"}, "test": {"name": "test", "num_bytes": 9080008, "num_examples": 10000, "dataset_name": "mnist"}}, "download_checksums": {"https://storage.googleapis.com/cvdf-datasets/mnist/train-images-idx3-ubyte.gz": {"num_bytes": 9912422, "checksum": "440fcabf73cc546fa21475e81ea370265605f56be210a4024d2ca8f203523609"}, "https://storage.googleapis.com/cvdf-datasets/mnist/train-labels-idx1-ubyte.gz": {"num_bytes": 28881, "checksum": "3552534a0a558bbed6aed32b30c495cca23d567ec52cac8be1a0730e8010255c"}, "https://storage.googleapis.com/cvdf-datasets/mnist/t10k-images-idx3-ubyte.gz": {"num_bytes": 1648877, "checksum": "8d422c7b0a1c1c79245a5bcf07fe86e33eeafee792b84584aec276f5a2dbc4e6"}, "https://storage.googleapis.com/cvdf-datasets/mnist/t10k-labels-idx1-ubyte.gz": {"num_bytes": 4542, "checksum": "f7ae60f92e00ec6debd23a6088c31dbd2371eca3ffa0defaefb259924204aec6"}}, "download_size": 11594722, "post_processing_size": null, "dataset_size": 63560056, "size_in_bytes": 75154778}}
+{"mnist": {"description": "The MNIST dataset consists of 70,000 28x28 black-and-white images in 10 classes (one for each digits), with 7,000\nimages per class. There are 60,000 training images and 10,000 test images.\n", "citation": "@article{lecun2010mnist,\n  title={MNIST handwritten digit database},\n  author={LeCun, Yann and Cortes, Corinna and Burges, CJ},\n  journal={ATT Labs [Online]. Available: http://yann.lecun.com/exdb/mnist},\n  volume={2},\n  year={2010}\n}\n", "homepage": "http://yann.lecun.com/exdb/mnist/", "license": "", "features": {"image": {"id": null, "_type": "Image"}, "label": {"num_classes": 10, "names": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": {"input": "image", "output": "label"}, "task_templates": [{"task": "image-classification", "image_column": "image", "label_column": "label", "labels": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"]}], "builder_name": "mnist", "config_name": "mnist", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 17470848, "num_examples": 60000, "dataset_name": "mnist"}, "test": {"name": "test", "num_bytes": 2916440, "num_examples": 10000, "dataset_name": "mnist"}}, "download_checksums": {"https://storage.googleapis.com/cvdf-datasets/mnist/train-images-idx3-ubyte.gz": {"num_bytes": 9912422, "checksum": "440fcabf73cc546fa21475e81ea370265605f56be210a4024d2ca8f203523609"}, "https://storage.googleapis.com/cvdf-datasets/mnist/train-labels-idx1-ubyte.gz": {"num_bytes": 28881, "checksum": "3552534a0a558bbed6aed32b30c495cca23d567ec52cac8be1a0730e8010255c"}, "https://storage.googleapis.com/cvdf-datasets/mnist/t10k-images-idx3-ubyte.gz": {"num_bytes": 1648877, "checksum": "8d422c7b0a1c1c79245a5bcf07fe86e33eeafee792b84584aec276f5a2dbc4e6"}, "https://storage.googleapis.com/cvdf-datasets/mnist/t10k-labels-idx1-ubyte.gz": {"num_bytes": 4542, "checksum": "f7ae60f92e00ec6debd23a6088c31dbd2371eca3ffa0defaefb259924204aec6"}}, "download_size": 11594722, "post_processing_size": null, "dataset_size": 20387288, "size_in_bytes": 31982010}}

mnist.py

@@ -22,6 +22,7 @@ import struct
 import numpy as np
 
 import datasets
+from datasets.tasks import ImageClassification
 
 
 _CITATION = """\
@@ -64,13 +65,20 @@ class MNIST(datasets.GeneratorBasedBuilder):
             description=_DESCRIPTION,
             features=datasets.Features(
                 {
-                    "image": datasets.Array2D(shape=(28, 28), dtype="uint8"),
+                    "image": datasets.Image(),
                     "label": datasets.features.ClassLabel(names=["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"]),
                 }
             ),
             supervised_keys=("image", "label"),
             homepage="http://yann.lecun.com/exdb/mnist/",
             citation=_CITATION,
+            task_templates=[
+                ImageClassification(
+                    image_column="image",
+                    label_column="label",
+                    labels=["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"],
+                )
+            ],
         )
 
     def _split_generators(self, dl_manager):