feat: add load script

wagons-images-classification.py

@@ -0,0 +1,76 @@
+from xml.etree import ElementTree as ET
+
+import datasets
+
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+title = {wagons-images-classification},
+author = {TrainingDataPro},
+year = {2023}
+}
+"""
+
+_DESCRIPTION = """\
+The dataset consists of images depicting **loaded and unloaded** wagons.
+The data are organasied in two folders for loaded and unloaded wagons and assisted with
+.CSV file containing text classification of the images.
+This dataset can be useful for various tasks, such as *image classification, object
+detection and data-driven analyses related to wagon loading and unloading processes.
+The dataset is useful for **rail transport sphere**, it can be utilised for automation
+the identification and classification of the wagons and further optimization of the
+processes in the industry.
+"""
+
+_NAME = "wagons-images-classification"
+
+_HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}"
+
+_LICENSE = ""
+
+_DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/"
+
+_LABELS = ["loaded", "unloaded"]
+
+
+class MinersDetection(datasets.GeneratorBasedBuilder):
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("int32"),
+                    "name": datasets.Value("string"),
+                    "image": datasets.Image(),
+                    "label": datasets.ClassLabel(
+                        num_classes=len(_LABELS),
+                        names=_LABELS,
+                    ),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        images = dl_manager.download(f"{_DATA}images.tar.gz")
+        images = dl_manager.iter_archive(images)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "images": images,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, images):
+        for idx, ((image_path, image)) in enumerate(images):
+            label = "unloaded" if "unloaded" in image_path else "loaded"
+            
+            yield idx, {
+                "id": idx,
+                "name": image_path,
+                "image": {"path": image_path, "bytes": image.read()},
+                "label": label,
+            }