diff --git a/annotations/test.csv b/annotations/test.csv
deleted file mode 100644
index 6c9b470cfb7aaa0ea8cbf632ea4e36a8dcbf654a..0000000000000000000000000000000000000000
--- a/annotations/test.csv
+++ /dev/null
@@ -1,1381 +0,0 @@
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diff --git a/annotations/train.csv b/annotations/train.csv
deleted file mode 100644
index 943dfceafa90fed5b832d7d8afe156ab514d7236..0000000000000000000000000000000000000000
--- a/annotations/train.csv
+++ /dev/null
@@ -1,7041 +0,0 @@
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-f85199c005a2e44c,0.328969,0.9918170000000001,0.0,0.98036,hot dog,hot dog
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-06e6d91815e6654a,0.019533000000000002,0.935111,0.17588199999999998,0.866123,hot dog,hot dog
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diff --git a/annotations/val.csv b/annotations/val.csv
deleted file mode 100644
index 489a54dbc55ca60c0685de1f2453cf39bc64dc04..0000000000000000000000000000000000000000
--- a/annotations/val.csv
+++ /dev/null
@@ -1,1418 +0,0 @@
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diff --git a/snacks.py b/snacks.py
new file mode 100644
index 0000000000000000000000000000000000000000..962189e7b545889a22e007d62a7d35480b2f5916
--- /dev/null
+++ b/snacks.py
@@ -0,0 +1,87 @@
+# 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"
+_ANNOTATIONS_URL = "https://huggingface.co/datasets/Matthijs/snacks/resolve/main/annotations.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="img", 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(),
+                }                
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