Create image-classification-yenthienviet.py

image-classification-yenthienviet.py

@@ -0,0 +1,103 @@
+
+"""TODO: Add a description here."""
+import csv
+import json
+import os
+
+import datasets
+from datasets.tasks import ImageClassification
+
+_DESCRIPTION = """\
+This dataset contains all THIENVIET products images split in training,
+    validation and testing
+"""
+
+_URLS = {
+    "train": "https://huggingface.co/datasets/chanelcolgate/image-classification-yenthienviet/resolve/main/data/train.zip",
+    "val": "https://huggingface.co/datasets/chanelcolgate/image-classification-yenthienviet/resolve/main/data/val.zip",
+    "test": "https://huggingface.co/datasets/chanelcolgate/image-classification-yenthienviet/resolve/main/data/test.zip"
+}
+
+_CATEGORIES  = ['botkhi','thuytinh','ocvit','ban','contrung','kimloai','toc']
+
+class YenthienvietConfig(datasets.BuilderConfig):
+    """Builder Config for image-classification-yenthienviet"""
+    def __init__(self, name, data_urls, **kwargs):
+        """
+        BuilderConfig for image-classification-yenthienviet.
+
+        Args:
+            data_urls: `dict`, name to url to download the zip file from.
+            **kwargs: keyword arguments forwared to super.
+        """
+        super().__init__(version=datasets.Version("1.0.0", **kwargs))
+        self.name
+        self.data_urls = data_urls
+
+# TODO: Name of the dataset usually matches the script name
+class YenthienvietClassification(datasets.GeneratorBasedBuilder):
+    """ Builder for image-classification-yenthienviet"""
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIG_CLASS = YenthienvietConfig
+    BUILDER_CONFIGS = [
+        YenthienvietConfig(
+            name="version-10/10",
+            description="Version 10/10 of image-classification-yenthienviet dataset.",
+            data_urls=_URLS,
+        )
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "image_file_path": datasets.Value("string"),
+                "image": datasets.Image(),
+                "labels": datasets.features.ClassLabel(names=_CATEGORIES)
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=("image", "label"),
+            task_templates=[ImageClassification(image_column="image", label_column="labels")]
+        )
+
+    def _split_generators(self, dl_manager):
+        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and exract URLS
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+        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["val"]]),
+                },
+            ),
+            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)),
+                }