add dataset loading scripts

class_names.txt

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+almond_butter
+almonds
+apple
+apricot
+asparagus
+avocado
+bacon
+bacon_and_egg_burger
+bagel
+baklava
+banana
+banana_bread
+barbecue_sauce
+beans
+beef
+beef_curry
+beef_mince
+beef_stir_fry
+beer
+beetroot
+biltong
+blackberries
+blueberries
+bok_choy
+bread
+broccoli
+broccolini
+brownie
+brussel_sprouts
+burrito
+butter
+cabbage
+calamari
+candy
+capsicum
+carrot
+cashews
+cauliflower
+celery
+cheese
+cheeseburger
+cherries
+chicken_breast
+chicken_thighs
+chicken_wings
+chilli
+chimichurri
+chocolate
+chocolate_cake
+coconut
+coffee
+coleslaw
+cookies
+coriander
+corn
+corn_chips
+cream
+croissant
+crumbed_chicken
+cucumber
+cupcake
+daikon_radish
+dates
+donuts
+dragonfruit
+eggplant
+eggs
+enoki_mushroom
+fennel
+figs
+french_toast
+fried_rice
+fries
+fruit_juice
+garlic
+garlic_bread
+ginger
+goji_berries
+granola
+grapefruit
+grapes
+green_beans
+green_onion
+guacamole
+guava
+gyoza
+ham
+honey
+hot_chocolate
+ice_coffee
+ice_cream
+iceberg_lettuce
+jerusalem_artichoke
+kale
+karaage_chicken
+kimchi
+kiwi_fruit
+lamb_chops
+leek
+lemon
+lentils
+lettuce
+lime
+mandarin
+mango
+maple_syrup
+mashed_potato
+mayonnaise
+milk
+miso_soup
+mushrooms
+nectarines
+noodles
+nuts
+olive_oil
+olives
+omelette
+onion
+orange
+orange_juice
+oysters
+pain_au_chocolat
+pancakes
+papaya
+parsley
+parsnips
+passionfruit
+pasta
+pawpaw
+peach
+pear
+peas
+pickles
+pineapple
+pizza
+plum
+pomegranate
+popcorn
+pork_belly
+pork_chop
+pork_loins
+porridge
+potato_bake
+potato_chips
+potato_scallop
+potatoes
+prawns
+pumpkin
+radish
+ramen
+raspberries
+red_onion
+red_wine
+rhubarb
+rice
+roast_beef
+roast_pork
+roast_potatoes
+rockmelon
+rosemary
+salad
+salami
+salmon
+salsa
+salt
+sandwich
+sardines
+sausage_roll
+sausages
+scrambled_eggs
+seaweed
+shallots
+snow_peas
+soda
+soy_sauce
+spaghetti_bolognese
+spinach
+sports_drink
+squash
+starfruit
+steak
+strawberries
+sushi
+sweet_potato
+tacos
+tamarillo
+taro
+tea
+toast
+tofu
+tomato
+tomato_chutney
+tomato_sauce
+turnip
+watermelon
+white_onion
+white_wine
+yoghurt
+zucchini

food_vision_199_classes.py

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+"""
+Loading script for the Food Vision 199 classes dataset.
+
+See the template: https://github.com/huggingface/datasets/blob/main/templates/new_dataset_script.py
+See the example for Food101: https://huggingface.co/datasets/food101/blob/main/food101.py 
+See another example: https://huggingface.co/datasets/davanstrien/encyclopedia_britannica/blob/main/encyclopedia_britannica.py
+"""
+
+import datasets
+import os
+import pandas as pd
+
+from datasets.tasks import ImageClassification
+
+_HOMEPAGE = "https://www.nutrify.app"
+_LICENSE = "TODO"
+_CITATION = "TODO"
+_DESCRIPTION = "Images of 199 food classes from the Nutrify app."
+
+# Read lines of class_names.txt
+with open("class_names.txt", "r") as f:
+    _NAMES = f.read().splitlines()
+
+class Food199(datasets.GeneratorBasedBuilder):
+    """Food199 Images dataset"""
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "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")],
+        )
+
+    def _split_generators(self, dl_manager):
+        """
+        This function returns the logic to split the dataset into different splits as well as labels.
+        """
+        csv = dl_manager.download("annotations.csv")
+        df = pd.read_csv(csv)
+        df_train_annotations = df[df["split"] == "train"].to_dict(orient="records")
+        df_test_annotations = df[df["split"] == "test"].to_dict(orient="records")
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "annotations": df_train_annotations,
+                }),
+            datasets.SplitGenerator(name=datasets.Split.TEST,
+                gen_kwargs={
+                    "annotations": df_test_annotations,
+                })]
+
+
+    def _generate_examples(self, annotations):
+        """
+        This function takes in the kwargs from the _split_generators method and can then yield information from them.
+        """
+        for id_, row in enumerate(annotations):
+            row["image"] = row.pop("filename")
+            row["label"] = row.pop("label")
+            yield id_, row
+