Create resistor.py

resistor.py

@@ -0,0 +1,101 @@
+import os
+
+import datasets
+from datasets.tasks import ImageClassification
+
+
+_HOMEPAGE = "https://huggingface.co/datasets/MithatGuner/resistor/"
+_LICENSE = "Public Domain"
+_CITATION = """\
+"""
+_CATEGORIES = ['converse', 'adidas', 'nike']
+
+
+class RESISTORConfig(datasets.BuilderConfig):
+    """Builder Config for resistor"""
+
+    def __init__(self, data_urls, **kwargs):
+        """
+        BuilderConfig for resistor.
+        Args:
+          data_urls: `dict`, name to url to download the zip file from.
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(RESISTORConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
+        self.data_urls = data_urls
+
+
+class SHOECLASSIFICATION(datasets.GeneratorBasedBuilder):
+    """resistor image classification dataset"""
+
+    VERSION = datasets.Version("1.0.0")
+    BUILDER_CONFIGS = [
+        RESISTORConfig(
+            name="full",
+            description="Full version of resistor dataset.",
+            data_urls={
+    "train": "https://huggingface.co/datasets/MithatGuner/resistor/resolve/main/data/train.zip",
+    "validation": "https://huggingface.co/datasets/MithatGuner/resistor/resolve/main/data/valid.zip",
+    "test": "https://huggingface.co/datasets/MithatGuner/resistor/resolve/main/data/test.zip",
+}
+,
+        ),
+        RESISTORConfig(
+            name="mini",
+            description="Mini version of resistor dataset.",
+            data_urls={
+                "train": "https://huggingface.co/datasets/MithatGuner/resistor/resolve/main/data/valid-mini.zip",
+                "validation": "https://huggingface.co/datasets/MithatGuner/resistor/resolve/main/data/valid-mini.zip",
+                "test": "https://huggingface.co/datasets/MithatGuner/resistor/resolve/main/data/valid-mini.zip",
+            },
+        )
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            features=datasets.Features(
+                {
+                    "image_file_path": datasets.Value("string"),
+                    "image": datasets.Image(),
+                    "labels": datasets.features.ClassLabel(names=_CATEGORIES),
+                }
+            ),
+            supervised_keys=("image", "labels"),
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+            license=_LICENSE,
+            task_templates=[ImageClassification(image_column="image", label_column="labels")],
+        )
+
+    def _split_generators(self, dl_manager):
+        data_files = dl_manager.download_and_extract(self.config.data_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", ".png", ".jpeg", ".bmp", ".tif", ".tiff")):
+                yield i, {
+                    "image_file_path": path,
+                    "image": path,
+                    "labels": os.path.basename(os.path.dirname(path)),
+                }