Update files from the datasets library (from 1.17.0)

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

README.md

@@ -75,7 +75,8 @@ A sample from the training set is provided below:
 
 ```
 {
-  'image': '/root/.cache/huggingface/datasets/downloads/extracted/6e1e8c9052e9f3f7ecbcb4b90860668f81c1d36d86cc9606d49066f8da8bfb4f/PetImages/Cat/1.jpg',
+  'image_file_path': '/root/.cache/huggingface/datasets/downloads/extracted/6e1e8c9052e9f3f7ecbcb4b90860668f81c1d36d86cc9606d49066f8da8bfb4f/PetImages/Cat/1.jpg',
+  'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x375 at 0x29CEAD71780>,
   'label': 0
 }
 ```
@@ -85,6 +86,7 @@ A sample from the training set is provided below:
 The data instances have the following fields:
 
 - `image_file_path`: a `string` filepath to an image.
+- `image`: A `PIL.Image.Image` object containing the 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]`.
 - `labels`: an `int` classification label.
 
 Class Label Mappings:

cats_vs_dogs.py

@@ -50,15 +50,12 @@ class CatsVsDogs(datasets.GeneratorBasedBuilder):
             features=datasets.Features(
                 {
                     "image_file_path": datasets.Value("string"),
+                    "image": datasets.Image(),
                     "labels": datasets.features.ClassLabel(names=["cat", "dog"]),
                 }
             ),
-            supervised_keys=("image_file_path", "labels"),
-            task_templates=[
-                ImageClassification(
-                    image_file_path_column="image_file_path", label_column="labels", labels=["cat", "dog"]
-                )
-            ],
+            supervised_keys=("image", "labels"),
+            task_templates=[ImageClassification(image_column="image", label_column="labels", labels=["cat", "dog"])],
             homepage=_HOMEPAGE,
             citation=_CITATION,
         )
@@ -76,6 +73,7 @@ class CatsVsDogs(datasets.GeneratorBasedBuilder):
                 if b"JFIF" in f.peek(10):
                     yield str(i), {
                         "image_file_path": str(filepath),
+                        "image": str(filepath),
                         "labels": filepath.parent.name.lower(),
                     }
                     continue

dataset_infos.json

@@ -1 +1 @@
-{"default": {"description": "A large set of images of cats and dogs. There are 1738 corrupted images that are dropped.", "citation": "@Inproceedings (Conference){asirra-a-captcha-that-exploits-interest-aligned-manual-image-categorization,\n    author = {Elson, Jeremy and Douceur, John (JD) and Howell, Jon and Saul, Jared},\n    title = {Asirra: A CAPTCHA that Exploits Interest-Aligned Manual Image Categorization},\n    booktitle = {Proceedings of 14th ACM Conference on Computer and Communications Security (CCS)},\n    year = {2007},\n    month = {October},\n    publisher = {Association for Computing Machinery, Inc.},\n    url = {https://www.microsoft.com/en-us/research/publication/asirra-a-captcha-that-exploits-interest-aligned-manual-image-categorization/},\n    edition = {Proceedings of 14th ACM Conference on Computer and Communications Security (CCS)},\n}\n", "homepage": "https://www.microsoft.com/en-us/download/details.aspx?id=54765", "license": "", "features": {"image_file_path": {"dtype": "string", "id": null, "_type": "Value"}, "labels": {"num_classes": 2, "names": ["cat", "dog"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": {"input": "image_file_path", "output": "labels"}, "task_templates": [{"task": "image-classification", "image_file_path_column": "image_file_path", "label_column": "labels", "labels": ["cat", "dog"]}], "builder_name": "cats_vs_dogs", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 3701417, "num_examples": 23410, "dataset_name": "cats_vs_dogs"}}, "download_checksums": {"https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip": {"num_bytes": 824894548, "checksum": "f9553e426bd725354ed3a27e3c6920caadb55c835d1ebd880d2e56d3f1fbb22b"}}, "download_size": 824894548, "post_processing_size": null, "dataset_size": 3701417, "size_in_bytes": 828595965}}
+{"default": {"description": "A large set of images of cats and dogs. There are 1738 corrupted images that are dropped.", "citation": "@Inproceedings (Conference){asirra-a-captcha-that-exploits-interest-aligned-manual-image-categorization,\n    author = {Elson, Jeremy and Douceur, John (JD) and Howell, Jon and Saul, Jared},\n    title = {Asirra: A CAPTCHA that Exploits Interest-Aligned Manual Image Categorization},\n    booktitle = {Proceedings of 14th ACM Conference on Computer and Communications Security (CCS)},\n    year = {2007},\n    month = {October},\n    publisher = {Association for Computing Machinery, Inc.},\n    url = {https://www.microsoft.com/en-us/research/publication/asirra-a-captcha-that-exploits-interest-aligned-manual-image-categorization/},\n    edition = {Proceedings of 14th ACM Conference on Computer and Communications Security (CCS)},\n}\n", "homepage": "https://www.microsoft.com/en-us/download/details.aspx?id=54765", "license": "", "features": {"image_file_path": {"dtype": "string", "id": null, "_type": "Value"}, "image": {"id": null, "_type": "Image"}, "labels": {"num_classes": 2, "names": ["cat", "dog"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": {"input": "image", "output": "labels"}, "task_templates": [{"task": "image-classification", "image_column": "image", "label_column": "labels", "labels": ["cat", "dog"]}], "builder_name": "cats_vs_dogs", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 7503250, "num_examples": 23422, "dataset_name": "cats_vs_dogs"}}, "download_checksums": {"https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip": {"num_bytes": 824894548, "checksum": "f9553e426bd725354ed3a27e3c6920caadb55c835d1ebd880d2e56d3f1fbb22b"}}, "download_size": 824894548, "post_processing_size": null, "dataset_size": 7503250, "size_in_bytes": 832397798}}