:tada: init

README.md

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+---
+annotations_creators:
+- crowdsourced
+language_creators:
+- crowdsourced
+languages:
+- en
+licenses:
+- unknown
+multilinguality:
+- monolingual
+pretty_name: food101
+size_categories:
+- 10K<n<100K
+source_datasets:
+- extended|other-foodspotting
+task_categories:
+- other
+task_ids:
+- other-other-image-classification
+paperswithcode_id: food-101
+---
+
+# Dataset Card for Food-101
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:**[Food-101 Dataset](https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/)
+- **Repository:**N/A
+- **Paper:**[Paper](https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/static/bossard_eccv14_food-101.pdf)
+- **Leaderboard:**N/A
+- **Point of Contact:**N/A
+
+### Dataset Summary
+
+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.
+
+### Supported Tasks and Leaderboards
+
+- image-classification
+
+### Languages
+
+English
+
+## Dataset Structure
+
+### Data Instances
+
+A sample from the training set is provided below:
+
+```
+{
+  'image': '/root/.cache/huggingface/datasets/downloads/extracted/6e1e8c9052e9f3f7ecbcb4b90860668f81c1d36d86cc9606d49066f8da8bfb4f/food-101/images/churros/1004234.jpg',
+  'label': 23
+}
+```
+
+### Data Fields
+
+The data instances have the following fields:
+
+- `image`: a `string` filepath to an image.
+- `label`: an `int` classification label.
+
+### Data Splits
+
+ 
+|   name   |train|validation|
+|----------|----:|---------:|
+|food101|75750|25250|
+
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+```
+ @inproceedings{bossard14,
+  title = {Food-101 -- Mining Discriminative Components with Random Forests},
+  author = {Bossard, Lukas and Guillaumin, Matthieu and Van Gool, Luc},
+  booktitle = {European Conference on Computer Vision},
+  year = {2014}
+}
+```
+
+### Contributions
+
+Thanks to [@nateraw](https://github.com/nateraw) for adding this dataset.

dataset_infos.json

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+{"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}}

food101.py

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+# coding=utf-8
+# Copyright 2021 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# 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"
+
+_HOMEPAGE = "https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/"
+
+_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,
+  title = {Food-101 -- Mining Discriminative Components with Random Forests},
+  author = {Bossard, Lukas and Guillaumin, Matthieu and Van Gool, Luc},
+  booktitle = {European Conference on Computer Vision},
+  year = {2014}
+}
+"""
+
+_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",
+]
+
+
+class Food101(datasets.GeneratorBasedBuilder):
+    """Food-101 Images dataset."""
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "image": datasets.Value("string"),
+                    "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,
+        )
+
+    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"
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"json_file_path": meta_path / "train.json", "image_dir_path": image_dir_path},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"json_file_path": meta_path / "test.json", "image_dir_path": image_dir_path},
+            ),
+        ]
+
+    def _generate_examples(self, json_file_path, image_dir_path):
+        """Generate images and labels for splits."""
+        labels = self.info.features["label"]
+        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": labels.encode_example(label)}
+                yield image_name, features