Update files from the datasets library (from 1.17.0)

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

README.md

@@ -76,7 +76,7 @@ A sample from the training set is provided below:
 
 ```
 {
-  'image': '/root/.cache/huggingface/datasets/downloads/extracted/6e1e8c9052e9f3f7ecbcb4b90860668f81c1d36d86cc9606d49066f8da8bfb4f/food-101/images/churros/1004234.jpg',
+  'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=384x512 at 0x276021C5EB8>,
   'label': 23
 }
 ```
@@ -85,7 +85,7 @@ A sample from the training set is provided below:
 
 The data instances have the following fields:
 
-- `image`: 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]`.
 - `label`: an `int` classification label.
 
 <details>

dataset_infos.json

@@ -1 +1 @@
-{"default": {"description": "This dataset consists of 101 food categories, with 101'000 images. For each class, 250 manually reviewed test images are provided as well as 750 training images. On purpose, the training images were not cleaned, and thus still contain some amount of noise. This comes mostly in the form of intense colors and sometimes wrong labels. All images were rescaled to have a maximum side length of 512 pixels.", "citation": " @inproceedings{bossard14,\n  title = {Food-101 -- Mining Discriminative Components with Random Forests},\n  author = {Bossard, Lukas and Guillaumin, Matthieu and Van Gool, Luc},\n  booktitle = {European Conference on Computer Vision},\n  year = {2014}\n}\n", "homepage": "https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/", "license": "LICENSE AGREEMENT\n=================\n - The Food-101 data set consists of images from Foodspotting [1] which are not\n   property of the Federal Institute of Technology Zurich (ETHZ). Any use beyond\n   scientific fair use must be negociated with the respective picture owners\n   according to the Foodspotting terms of use [2].\n\n[1] http://www.foodspotting.com/\n[2] http://www.foodspotting.com/terms/\n", "features": {"image": {"filename": {"dtype": "string", "id": null, "_type": "Value"}, "data": {"dtype": "binary", "id": null, "_type": "Value"}}, "label": {"num_classes": 101, "names": ["apple_pie", "baby_back_ribs", "baklava", "beef_carpaccio", "beef_tartare", "beet_salad", "beignets", "bibimbap", "bread_pudding", "breakfast_burrito", "bruschetta", "caesar_salad", "cannoli", "caprese_salad", "carrot_cake", "ceviche", "cheesecake", "cheese_plate", "chicken_curry", "chicken_quesadilla", "chicken_wings", "chocolate_cake", "chocolate_mousse", "churros", "clam_chowder", "club_sandwich", "crab_cakes", "creme_brulee", "croque_madame", "cup_cakes", "deviled_eggs", "donuts", "dumplings", "edamame", "eggs_benedict", "escargots", "falafel", "filet_mignon", "fish_and_chips", "foie_gras", "french_fries", "french_onion_soup", "french_toast", "fried_calamari", "fried_rice", "frozen_yogurt", "garlic_bread", "gnocchi", "greek_salad", "grilled_cheese_sandwich", "grilled_salmon", "guacamole", "gyoza", "hamburger", "hot_and_sour_soup", "hot_dog", "huevos_rancheros", "hummus", "ice_cream", "lasagna", "lobster_bisque", "lobster_roll_sandwich", "macaroni_and_cheese", "macarons", "miso_soup", "mussels", "nachos", "omelette", "onion_rings", "oysters", "pad_thai", "paella", "pancakes", "panna_cotta", "peking_duck", "pho", "pizza", "pork_chop", "poutine", "prime_rib", "pulled_pork_sandwich", "ramen", "ravioli", "red_velvet_cake", "risotto", "samosa", "sashimi", "scallops", "seaweed_salad", "shrimp_and_grits", "spaghetti_bolognese", "spaghetti_carbonara", "spring_rolls", "steak", "strawberry_shortcake", "sushi", "tacos", "takoyaki", "tiramisu", "tuna_tartare", "waffles"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": {"input": "image", "output": "label"}, "task_templates": null, "builder_name": "food101", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 3843765322, "num_examples": 75750, "dataset_name": "food101"}, "validation": {"name": "validation", "num_bytes": 1275549954, "num_examples": 25250, "dataset_name": "food101"}}, "download_checksums": {"http://data.vision.ee.ethz.ch/cvl/food-101.tar.gz": {"num_bytes": 4996278331, "checksum": "d97d15e438b7f4498f96086a4f7e2fa42a32f2712e87d3295441b2b6314053a4"}, "https://s3.amazonaws.com/datasets.huggingface.co/food101/meta/train.txt": {"num_bytes": 1468812, "checksum": "2920f7d55473974492b41a01241ccfd71df1b74d29d27b617337f840f58f77ab"}, "https://s3.amazonaws.com/datasets.huggingface.co/food101/meta/test.txt": {"num_bytes": 489429, "checksum": "440d53374697d019a972fe66e8e44031ae80267a126ecb814ad537ec1fd506db"}}, "download_size": 4998236572, "post_processing_size": null, "dataset_size": 5119315276, "size_in_bytes": 10117551848}}
+{"default": {"description": "This dataset consists of 101 food categories, with 101'000 images. For each class, 250 manually reviewed test images are provided as well as 750 training images. On purpose, the training images were not cleaned, and thus still contain some amount of noise. This comes mostly in the form of intense colors and sometimes wrong labels. All images were rescaled to have a maximum side length of 512 pixels.", "citation": " @inproceedings{bossard14,\n  title = {Food-101 -- Mining Discriminative Components with Random Forests},\n  author = {Bossard, Lukas and Guillaumin, Matthieu and Van Gool, Luc},\n  booktitle = {European Conference on Computer Vision},\n  year = {2014}\n}\n", "homepage": "https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/", "license": "LICENSE AGREEMENT\n=================\n - The Food-101 data set consists of images from Foodspotting [1] which are not\n   property of the Federal Institute of Technology Zurich (ETHZ). Any use beyond\n   scientific fair use must be negociated with the respective picture owners\n   according to the Foodspotting terms of use [2].\n\n[1] http://www.foodspotting.com/\n[2] http://www.foodspotting.com/terms/\n", "features": {"image": {"id": null, "_type": "Image"}, "label": {"num_classes": 101, "names": ["apple_pie", "baby_back_ribs", "baklava", "beef_carpaccio", "beef_tartare", "beet_salad", "beignets", "bibimbap", "bread_pudding", "breakfast_burrito", "bruschetta", "caesar_salad", "cannoli", "caprese_salad", "carrot_cake", "ceviche", "cheesecake", "cheese_plate", "chicken_curry", "chicken_quesadilla", "chicken_wings", "chocolate_cake", "chocolate_mousse", "churros", "clam_chowder", "club_sandwich", "crab_cakes", "creme_brulee", "croque_madame", "cup_cakes", "deviled_eggs", "donuts", "dumplings", "edamame", "eggs_benedict", "escargots", "falafel", "filet_mignon", "fish_and_chips", "foie_gras", "french_fries", "french_onion_soup", "french_toast", "fried_calamari", "fried_rice", "frozen_yogurt", "garlic_bread", "gnocchi", "greek_salad", "grilled_cheese_sandwich", "grilled_salmon", "guacamole", "gyoza", "hamburger", "hot_and_sour_soup", "hot_dog", "huevos_rancheros", "hummus", "ice_cream", "lasagna", "lobster_bisque", "lobster_roll_sandwich", "macaroni_and_cheese", "macarons", "miso_soup", "mussels", "nachos", "omelette", "onion_rings", "oysters", "pad_thai", "paella", "pancakes", "panna_cotta", "peking_duck", "pho", "pizza", "pork_chop", "poutine", "prime_rib", "pulled_pork_sandwich", "ramen", "ravioli", "red_velvet_cake", "risotto", "samosa", "sashimi", "scallops", "seaweed_salad", "shrimp_and_grits", "spaghetti_bolognese", "spaghetti_carbonara", "spring_rolls", "steak", "strawberry_shortcake", "sushi", "tacos", "takoyaki", "tiramisu", "tuna_tartare", "waffles"], "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": ["apple_pie", "baby_back_ribs", "baklava", "beef_carpaccio", "beef_tartare", "beet_salad", "beignets", "bibimbap", "bread_pudding", "breakfast_burrito", "bruschetta", "caesar_salad", "cannoli", "caprese_salad", "carrot_cake", "ceviche", "cheese_plate", "cheesecake", "chicken_curry", "chicken_quesadilla", "chicken_wings", "chocolate_cake", "chocolate_mousse", "churros", "clam_chowder", "club_sandwich", "crab_cakes", "creme_brulee", "croque_madame", "cup_cakes", "deviled_eggs", "donuts", "dumplings", "edamame", "eggs_benedict", "escargots", "falafel", "filet_mignon", "fish_and_chips", "foie_gras", "french_fries", "french_onion_soup", "french_toast", "fried_calamari", "fried_rice", "frozen_yogurt", "garlic_bread", "gnocchi", "greek_salad", "grilled_cheese_sandwich", "grilled_salmon", "guacamole", "gyoza", "hamburger", "hot_and_sour_soup", "hot_dog", "huevos_rancheros", "hummus", "ice_cream", "lasagna", "lobster_bisque", "lobster_roll_sandwich", "macaroni_and_cheese", "macarons", "miso_soup", "mussels", "nachos", "omelette", "onion_rings", "oysters", "pad_thai", "paella", "pancakes", "panna_cotta", "peking_duck", "pho", "pizza", "pork_chop", "poutine", "prime_rib", "pulled_pork_sandwich", "ramen", "ravioli", "red_velvet_cake", "risotto", "samosa", "sashimi", "scallops", "seaweed_salad", "shrimp_and_grits", "spaghetti_bolognese", "spaghetti_carbonara", "spring_rolls", "steak", "strawberry_shortcake", "sushi", "tacos", "takoyaki", "tiramisu", "tuna_tartare", "waffles"]}], "builder_name": "food101", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 3845865322, "num_examples": 75750, "dataset_name": "food101"}, "validation": {"name": "validation", "num_bytes": 1276249954, "num_examples": 25250, "dataset_name": "food101"}}, "download_checksums": {"http://data.vision.ee.ethz.ch/cvl/food-101.tar.gz": {"num_bytes": 4996278331, "checksum": "d97d15e438b7f4498f96086a4f7e2fa42a32f2712e87d3295441b2b6314053a4"}, "https://s3.amazonaws.com/datasets.huggingface.co/food101/meta/train.txt": {"num_bytes": 1468812, "checksum": "2920f7d55473974492b41a01241ccfd71df1b74d29d27b617337f840f58f77ab"}, "https://s3.amazonaws.com/datasets.huggingface.co/food101/meta/test.txt": {"num_bytes": 489429, "checksum": "440d53374697d019a972fe66e8e44031ae80267a126ecb814ad537ec1fd506db"}}, "download_size": 4998236572, "post_processing_size": null, "dataset_size": 5122115276, "size_in_bytes": 10120351848}}

food101.py

@@ -15,6 +15,7 @@
 """Dataset class for Food-101 dataset."""
 
 import datasets
+from datasets.tasks import ImageClassification
 
 
 _BASE_URL = "http://data.vision.ee.ethz.ch/cvl/food-101.tar.gz"
@@ -171,14 +172,15 @@ class Food101(datasets.GeneratorBasedBuilder):
             description=_DESCRIPTION,
             features=datasets.Features(
                 {
-                    "image": {"filename": datasets.Value("string"), "data": datasets.Value("binary")},
-                    "label": datasets.features.ClassLabel(names=_NAMES),
+                    "image": datasets.Image(),
+                    "label": datasets.ClassLabel(names=_NAMES),
                 }
             ),
             supervised_keys=("image", "label"),
             homepage=_HOMEPAGE,
             citation=_CITATION,
             license=_LICENSE,
+            task_templates=[ImageClassification(image_column="image", label_column="label", labels=_NAMES)],
         )
 
     def _split_generators(self, dl_manager):
@@ -210,6 +212,6 @@ class Food101(datasets.GeneratorBasedBuilder):
                 if file_path[len(_IMAGES_DIR) : -len(".jpg")] in files_to_keep:
                     label = file_path.split("/")[2]
                     yield file_path, {
-                        "image": {"filename": file_path.split("/")[-1], "data": file_obj.read()},
+                        "image": {"path": file_path, "bytes": file_obj.read()},
                         "label": label,
                     }