fist dataset version

data/food101_raw.tar.gz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0747e577985e9886d13f7c7399c397282ffeb36dd36efa52504effbae4a05a94
+size 4139803338

food101-enriched.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 datasets
+from datasets.tasks import ImageClassification
+
+import pandas as pd
+
+from pathlib import Path
+from datasets import load_dataset
+
+
+_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 = (
+    "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}
+}
+"""
+
+_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",
+    "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",
+]
+
+_DATA_URLS = {
+'raw': "data/food101_raw.tar.gz",
+'metadata': 'data/food101-metadata.parquet.gzip'
+}
+
+
+
+class Food101Enriched(datasets.GeneratorBasedBuilder):
+    """Food101Enriched Data Set"""
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="enriched",
+            version=datasets.Version("1.0.0", ""),
+            description="Import of enriched Food 101 Data Set",
+        )
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "image": datasets.Value("string"),                   
+                    "label": datasets.features.ClassLabel(names=_NAMES),
+                    #"label_str": datasets.Value("string"),                   
+                    "split": datasets.Value("string"),                    
+                }
+            ),
+            supervised_keys=("image", "label"),
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+            license=_LICENSE,
+            task_templates=[ImageClassification(image_column="image", label_column="label")],
+        )
+
+    def _split_generators(self, dl_manager):
+        
+        archive_path = dl_manager.download_and_extract(_DATA_URLS['raw'])
+        metadata = pd.read_parquet(dl_manager.download(_DATA_URLS['metadata']))        
+    
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN, gen_kwargs={"archive_path": archive_path, 'metadata': metadata, "split": "train"}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST, gen_kwargs={"archive_path": archive_path, 'metadata': metadata, "split": "test"}
+            ),            
+        ]
+
+    def _generate_examples(self, archive_path, metadata, split):
+        """This function returns the examples in the raw (text) form."""
+
+        if split == "train":
+            df = metadata[metadata['split']=='train']
+
+        if split == "test":
+            df = metadata[metadata['split']=='test']
+
+        if split == "all":
+            df = metadata      
+
+        
+
+       
+        for index, row in df.iterrows():
+            img_path = archive_path + "/" + row['image']
+            #img = {"path": img_path, "bytes": None}
+            #print(str(len(row['probabilities'])))
+            #print(str(index))
+            result = {
+                'image': img_path,                
+                'label': row['label'],             
+                #'label_str': row['fine_label_str'],               
+                'split': split,            
+            }
+            yield index, result
+
+       
+
+
+
+
+
+if __name__ == "__main__":
+    ds = load_dataset("food101-enriched.py", split="all")