[DEV]Added a custom loader

Custom loader to load all the data

loading.py

@@ -0,0 +1,60 @@
+"""stanford-dogs: The Stanford Dogs Dataset."""
+
+from ast import literal_eval
+from pathlib import Path
+
+import datasets
+import pandas as pd
+
+logger = datasets.logging.get_logger(__name__)
+
+_DESCRIPTION = """
+The Stanford Dogs dataset contains images of 120 breeds of dogs from around the world. This dataset has been built using images and annotation from ImageNet for the task of fine-grained image categorization.
+"""
+
+_URL = "https://huggingface.co/datasets/Alanox/stanford-dogs"
+_IMAGES = _URL + "/resolve/main/images.tar.gz"
+_METADATA = _URL + "/resolve/main/metadata.csv"
+
+
+class StanfordDogs(datasets.GeneratorBasedBuilder):
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "name": datasets.Value("string"),
+                    "annotations": datasets.Array2D(shape=(None, 4), dtype="int32"),
+                    "target": datasets.Value("string"),
+                    "image": datasets.Image(),
+                }
+            ),
+            homepage="https://huggingface.co/datasets/Alanox/stanford-dogs",
+        )
+
+    def _split_generators(self, dl_manager):
+        images_archive = dl_manager.download(_IMAGES)
+        images = dl_manager.iter_archive(images_archive)
+
+        metadata_csv = dl_manager.download(_METADATA)
+        metadata = pd.read_csv(metadata_csv, on_bad_lines="skip").set_index("name")
+        metadata["annotations"] = metadata["annotations"].apply(literal_eval)
+
+        return [
+            datasets.SplitGenerator(
+                name="full",
+                gen_kwargs={"images": images, "metadata": metadata},
+            ),
+        ]
+
+    def _generate_examples(self, images, metadata: pd.DataFrame):
+        for i, (filepath, image) in enumerate(images):
+            filename = Path(filepath).name
+            item = metadata.loc[filename]
+
+            yield i, {
+                "name": filename,
+                "image": {"path": filepath, "bytes": image.read()},
+                "annotations": item["annotations"],
+                "target": item["target"],
+            }