Upload Eye_diabetic.py with huggingface_hub

Eye_diabetic.py

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+import os
+
+import datasets
+from datasets.tasks import ImageClassification
+
+
+_HOMEPAGE = "https://github.com/AI-Lab-Makerere/ibean/"
+
+_CITATION = """\
+@ONLINE {beansdata,
+    author="Makerere AI Lab",
+    title="Bean disease dataset",
+    month="January",
+    year="2020",
+    url="https://github.com/AI-Lab-Makerere/ibean/"
+}
+"""
+
+_DESCRIPTION = """\
+Beans is a dataset of images of beans taken in the field using smartphone
+cameras. It consists of 3 classes: 2 disease classes and the healthy class.
+Diseases depicted include Angular Leaf Spot and Bean Rust. Data was annotated
+by experts from the National Crops Resources Research Institute (NaCRRI) in
+Uganda and collected by the Makerere AI research lab.
+"""
+
+_URLS = {
+    "train": "https://huggingface.co/datasets/NawinCom/Eye_diabetic/resolve/main/train_val2/train.zip",
+    "validation": "https://huggingface.co/datasets/NawinCom/Eye_diabetic/resolve/main/train_val2/val.zip"
+}
+
+_NAMES = ["0", "1", "2", "3", "4"]
+
+
+class Beans(datasets.GeneratorBasedBuilder):
+    """Beans plant leaf images dataset."""
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "image_file_path": datasets.Value("string"),
+                    "image": datasets.Image(),
+                    "labels": datasets.features.ClassLabel(names=_NAMES),
+                }
+            ),
+            supervised_keys=("image", "labels"),
+            citation=_CITATION,
+            task_templates=[ImageClassification(image_column="image", label_column="labels")],
+        )
+
+    def _split_generators(self, dl_manager):
+        data_files = dl_manager.download_and_extract(_URLS)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "files": dl_manager.iter_files([data_files["train"]]),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "files": dl_manager.iter_files([data_files["validation"]]),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "files": dl_manager.iter_files([data_files["test"]]),
+                },
+            ),
+        ]
+
+    def _generate_examples(self, files):
+        for i, path in enumerate(files):
+            file_name = os.path.basename(path)
+            if file_name.endswith(".jpg"):
+                yield i, {
+                    "image_file_path": path,
+                    "image": path,
+                    "labels": os.path.basename(os.path.dirname(path)).lower(),
+                }