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.gitattributes

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-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bin.* filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
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-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
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-*.tgz filter=lfs diff=lfs merge=lfs -text
-*.wasm filter=lfs diff=lfs merge=lfs -text
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-*.zstandard filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
-# Audio files - uncompressed
-*.pcm filter=lfs diff=lfs merge=lfs -text
-*.sam filter=lfs diff=lfs merge=lfs -text
-*.raw filter=lfs diff=lfs merge=lfs -text
-# Audio files - compressed
-*.aac filter=lfs diff=lfs merge=lfs -text
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.gitignore

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-.DS_Store
-

README.md

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----
-pretty_name: Snacks
-task_categories:
-- image-classification
-- computer-vision
-license: cc-by-4.0
----
-
-# Dataset Card for Snacks
-
-## Dataset Summary
-
-This is a dataset of 20 different types of snack foods that accompanies the book [Machine Learning by Tutorials](https://www.raywenderlich.com/books/machine-learning-by-tutorials/v2.0).
-
-The images were taken from the [Google Open Images dataset](https://storage.googleapis.com/openimages/web/index.html), release 2017_11. 
-
-## Dataset Structure
-
-Number of images in the train/validation/test splits:
-
-```nohighlight
-train    4838
-val      955
-test     952
-total    6745
-```
-
-Total images in each category:
-
-```nohighlight
-apple         350
-banana        350
-cake          349
-candy         349
-carrot        349
-cookie        349
-doughnut      350
-grape         350
-hot dog       350
-ice cream     350
-juice         350
-muffin        348
-orange        349
-pineapple     340
-popcorn       260
-pretzel       204
-salad         350
-strawberry    348
-waffle        350
-watermelon    350
-```
-
-To save space in the download, the images were resized so that their smallest side is 256 pixels. All EXIF information was removed.
-
-### Data Splits
-
-Train, Test, Validation
-
-## Licensing Information
-
-Just like the images from Google Open Images, the snacks dataset is licensed under the terms of the Creative Commons license. 
-
-The images are listed as having a [CC BY 2.0](https://creativecommons.org/licenses/by/2.0/) license. 
-
-The annotations are licensed by Google Inc. under a [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license. 
-
-The **credits.csv** file contains the original URL, author information and license for each image.

images.zip → default/snacks-test.parquet

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-size 110279045
+oid sha256:39f51e4e3efa990edcf437df3f6e0516dfa22e5eb78383f64121e87136fafa8a
+size 14917207

default/snacks-train.parquet

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default/snacks-validation.parquet

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snacks.py

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-# coding=utf-8
-"""Snacks Data Set"""
-
-import os
-
-import datasets
-from datasets.tasks import ImageClassification
-
-
-_CITATION = """
-@article{OpenImages2,
-  title={OpenImages: A public dataset for large-scale multi-label and multi-class image classification.},
-  author={Krasin, Ivan and Duerig, Tom and Alldrin, Neil and Ferrari, Vittorio and Abu-El-Haija, Sami and Kuznetsova, Alina and Rom, Hassan and Uijlings, Jasper and Popov, Stefan and Kamali, Shahab and Malloci, Matteo and Pont-Tuset, Jordi and Veit, Andreas and Belongie, Serge and Gomes, Victor and Gupta, Abhinav and Sun, Chen and Chechik, Gal and Cai, David and Feng, Zheyun and Narayanan, Dhyanesh and Murphy, Kevin},
-  journal={Dataset available from https://storage.googleapis.com/openimages/web/index.html},
-  year={2017}
-}
-"""
-
-_DESCRIPTION = "This is a dataset of 20 different types of snack foods that accompanies the book Machine Learning by Tutorials, https://www.raywenderlich.com/books/machine-learning-by-tutorials/v2.0 — Based on images from Google Open Images dataset."
-
-_HOMEPAGE = "https://huggingface.co/datasets/Matthijs/snacks/"
-
-_LICENSE = "cc-by-4.0"
-
-_IMAGES_URL = "https://huggingface.co/datasets/Matthijs/snacks/resolve/main/images.zip"
-
-_NAMES = ["apple", "banana", "cake", "candy", "carrot", "cookie", 
-          "doughnut", "grape", "hot dog", "ice cream", "juice", 
-          "muffin", "orange", "pineapple", "popcorn", "pretzel",
-          "salad", "strawberry", "waffle", "watermelon"]
-
-
-class Snacks(datasets.GeneratorBasedBuilder):
-    """Snacks Data Set"""
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name="default",
-            version=datasets.Version("0.0.1", ""),
-            description="",
-        )
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    #"image_file_path": datasets.Value("string"),
-                    "image": datasets.Image(),
-                    "label": datasets.features.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):
-        archive = os.path.join(dl_manager.download_and_extract(_IMAGES_URL), "data")
-        train_path = os.path.join(archive, "train")
-        test_path = os.path.join(archive, "test")
-        val_path = os.path.join(archive, "val")
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN, gen_kwargs={"files": dl_manager.iter_files(train_path)}
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST, gen_kwargs={"files": dl_manager.iter_files(test_path)}
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION, gen_kwargs={"files": dl_manager.iter_files(val_path)}
-            ),
-        ]
-
-    def _generate_examples(self, files):
-        for i, file in enumerate(files):
-            if os.path.basename(file).endswith(".jpg"):
-                yield str(i), {
-                    #"image_file_path": file,                    
-                    "image": file,
-                    "label": os.path.basename(os.path.dirname(file)).lower(),
-                }