Update files from the datasets library (from 1.14.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.14.0

README.md

@@ -259,7 +259,16 @@ The data instances have the following fields:
 
 ### Licensing Information
 
-[More Information Needed]
+LICENSE AGREEMENT
+=================
+ - The Food-101 data set consists of images from Foodspotting [1] which are not
+   property of the Federal Institute of Technology Zurich (ETHZ). Any use beyond
+   scientific fair use must be negociated with the respective picture owners
+   according to the Foodspotting terms of use [2].
+
+[1] http://www.foodspotting.com/
+[2] http://www.foodspotting.com/terms/
+
 
 ### Citation Information
 

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": "", "features": {"image": {"dtype": "string", "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": [{"task": "image-classification", "image_file_path_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": 13210094, "num_examples": 75750, "dataset_name": "food101"}, "validation": {"name": "validation", "num_bytes": 4403191, "num_examples": 25250, "dataset_name": "food101"}}, "download_checksums": {"http://data.vision.ee.ethz.ch/cvl/food-101.tar.gz": {"num_bytes": 4996278331, "checksum": "d97d15e438b7f4498f96086a4f7e2fa42a32f2712e87d3295441b2b6314053a4"}}, "download_size": 4996278331, "post_processing_size": null, "dataset_size": 17613285, "size_in_bytes": 5013891616}}
+{"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}}

dummy/0.0.0/dummy_data.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:c75fa3428f1705c7b7390392422b3a952a18beddcde785af2663dd96bc84571b
-size 715637
+oid sha256:65c32f851cd9ed4131896fea0254b6cb8adb8a9dab1b82a0adec5b8ef1af70b1
+size 348186

food101.py

@@ -14,15 +14,16 @@
 # limitations under the License.
 """Dataset class for Food-101 dataset."""
 
-import json
-from pathlib import Path
-
 import datasets
-from datasets.tasks import ImageClassification
 
 
 _BASE_URL = "http://data.vision.ee.ethz.ch/cvl/food-101.tar.gz"
 
+_METADATA_URLS = {
+    "train": "https://s3.amazonaws.com/datasets.huggingface.co/food101/meta/train.txt",
+    "test": "https://s3.amazonaws.com/datasets.huggingface.co/food101/meta/test.txt",
+}
+
 _HOMEPAGE = "https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/"
 
 _DESCRIPTION = (
@@ -43,6 +44,18 @@ _CITATION = """\
 }
 """
 
+_LICENSE = """\
+LICENSE AGREEMENT
+=================
+ - The Food-101 data set consists of images from Foodspotting [1] which are not
+   property of the Federal Institute of Technology Zurich (ETHZ). Any use beyond
+   scientific fair use must be negociated with the respective picture owners
+   according to the Foodspotting terms of use [2].
+
+[1] http://www.foodspotting.com/
+[2] http://www.foodspotting.com/terms/
+"""
+
 _NAMES = [
     "apple_pie",
     "baby_back_ribs",
@@ -147,6 +160,8 @@ _NAMES = [
     "waffles",
 ]
 
+_IMAGES_DIR = "food-101/images/"
+
 
 class Food101(datasets.GeneratorBasedBuilder):
     """Food-101 Images dataset."""
@@ -156,36 +171,45 @@ class Food101(datasets.GeneratorBasedBuilder):
             description=_DESCRIPTION,
             features=datasets.Features(
                 {
-                    "image": datasets.Value("string"),
+                    "image": {"filename": datasets.Value("string"), "data": datasets.Value("binary")},
                     "label": datasets.features.ClassLabel(names=_NAMES),
                 }
             ),
             supervised_keys=("image", "label"),
             homepage=_HOMEPAGE,
-            task_templates=[ImageClassification(image_file_path_column="image", label_column="label", labels=_NAMES)],
             citation=_CITATION,
+            license=_LICENSE,
         )
 
     def _split_generators(self, dl_manager):
-        dl_path = Path(dl_manager.download_and_extract(_BASE_URL))
-        meta_path = dl_path / "food-101" / "meta"
-        image_dir_path = dl_path / "food-101" / "images"
+        archive_path = dl_manager.download(_BASE_URL)
+        split_metadata_paths = dl_manager.download(_METADATA_URLS)
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
-                gen_kwargs={"json_file_path": meta_path / "train.json", "image_dir_path": image_dir_path},
+                gen_kwargs={
+                    "images": dl_manager.iter_archive(archive_path),
+                    "metadata_path": split_metadata_paths["train"],
+                },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
-                gen_kwargs={"json_file_path": meta_path / "test.json", "image_dir_path": image_dir_path},
+                gen_kwargs={
+                    "images": dl_manager.iter_archive(archive_path),
+                    "metadata_path": split_metadata_paths["test"],
+                },
             ),
         ]
 
-    def _generate_examples(self, json_file_path, image_dir_path):
+    def _generate_examples(self, images, metadata_path):
         """Generate images and labels for splits."""
-        data = json.loads(json_file_path.read_text())
-        for label, images in data.items():
-            for image_name in images:
-                image = image_dir_path / f"{image_name}.jpg"
-                features = {"image": str(image), "label": label}
-                yield image_name, features
+        with open(metadata_path, encoding="utf-8") as f:
+            files_to_keep = set(f.read().split("\n"))
+        for file_path, file_obj in images:
+            if file_path.startswith(_IMAGES_DIR):
+                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()},
+                        "label": label,
+                    }