Create tiny-imagenet-c.py

tiny-imagenet-c.py

@@ -0,0 +1,91 @@
+import os
+import datasets
+
+
+class TinyImagenetC(datasets.GeneratorBasedBuilder):
+    """
+    Tiny ImageNet-C dataset for image classification tasks with corruptions applied.
+    """
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description="""The Tiny ImageNet-C dataset applies multiple corruptions to the Tiny ImageNet images. It 
+            includes 200 classes and various corruption levels.""",
+            features=datasets.Features(
+                {
+                    "image": datasets.Image(),
+                    "label": datasets.ClassLabel(names=self._labels()),
+                    "corruption_name": datasets.Value("string"),
+                    "corruption_level": datasets.Value("int32"),
+                }
+            ),
+            supervised_keys=("image", "label"),
+            homepage="https://zenodo.org/records/2536630",
+            citation="""@article{hendrycks2019robustness,
+                        title={Benchmarking Neural Network Robustness to Common Corruptions and Perturbations},
+                        author={Dan Hendrycks and Thomas Dietterich},
+                        journal={Proceedings of the International Conference on Learning Representations},
+                        year={2019}}""",
+            license="CC BY 4.0",
+        )
+
+    def _split_generators(self, dl_manager):
+        url = "https://zenodo.org/records/2536630/files/Tiny-ImageNet-C.tar?download=1"
+        archive_path = dl_manager.download_and_extract(url)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"archive_path": archive_path},
+            ),
+        ]
+
+    def _generate_examples(self, archive_path):
+        base_path = os.path.join(archive_path, "Tiny-ImageNet-C")
+        for root, _, files in os.walk(base_path):
+            for file_name in files:
+                if file_name.endswith(".JPEG"):
+                    full_path = os.path.join(root, file_name)
+                    parts = full_path.split(os.sep)
+                    corruption_name = parts[-4]
+                    corruption_level = int(parts[-3])
+                    label = parts[-2]
+
+                    yield full_path, {
+                        "image": full_path,
+                        "label": label,
+                        "corruption_name": corruption_name,
+                        "corruption_level": corruption_level,
+                    }
+
+    @staticmethod
+    def _labels():
+        return [
+            "n02124075", "n04067472", "n04540053", "n04099969", "n07749582", "n01641577", "n02802426", "n09246464",
+            "n07920052", "n03970156", "n03891332", "n02106662", "n03201208", "n02279972", "n02132136", "n04146614",
+            "n07873807", "n02364673", "n04507155", "n03854065", "n03838899", "n03733131", "n01443537", "n07875152",
+            "n03544143", "n09428293", "n03085013", "n02437312", "n07614500", "n03804744", "n04265275", "n02963159",
+            "n02486410", "n01944390", "n09256479", "n02058221", "n04275548", "n02321529", "n02769748", "n02099712",
+            "n07695742", "n02056570", "n02281406", "n01774750", "n02509815", "n03983396", "n07753592", "n04254777",
+            "n02233338", "n04008634", "n02823428", "n02236044", "n03393912", "n07583066", "n04074963", "n01629819",
+            "n09332890", "n02481823", "n03902125", "n03404251", "n09193705", "n03637318", "n04456115", "n02666196",
+            "n03796401", "n02795169", "n02123045", "n01855672", "n01882714", "n02917067", "n02988304", "n04398044",
+            "n02843684", "n02423022", "n02669723", "n04465501", "n02165456", "n03770439", "n02099601", "n04486054",
+            "n02950826", "n03814639", "n04259630", "n03424325", "n02948072", "n03179701", "n03400231", "n02206856",
+            "n03160309", "n01984695", "n03977966", "n03584254", "n04023962", "n02814860", "n01910747", "n04596742",
+            "n03992509", "n04133789", "n03937543", "n02927161", "n01945685", "n02395406", "n02125311", "n03126707",
+            "n04532106", "n02268443", "n02977058", "n07734744", "n03599486", "n04562935", "n03014705", "n04251144",
+            "n04356056", "n02190166", "n03670208", "n02002724", "n02074367", "n04285008", "n04560804", "n04366367",
+            "n02403003", "n07615774", "n04501370", "n03026506", "n02906734", "n01770393", "n04597913", "n03930313",
+            "n04118538", "n04179913", "n04311004", "n02123394", "n04070727", "n02793495", "n02730930", "n02094433",
+            "n04371430", "n04328186", "n03649909", "n04417672", "n03388043", "n01774384", "n02837789", "n07579787",
+            "n04399382", "n02791270", "n03089624", "n02814533", "n04149813", "n07747607", "n03355925", "n01983481",
+            "n04487081", "n03250847", "n03255030", "n02892201", "n02883205", "n03100240", "n02415577", "n02480495",
+            "n01698640", "n01784675", "n04376876", "n03444034", "n01917289", "n01950731", "n03042490", "n07711569",
+            "n04532670", "n03763968", "n07768694", "n02999410", "n03617480", "n06596364", "n01768244", "n02410509",
+            "n03976657", "n01742172", "n03980874", "n02808440", "n02226429", "n02231487", "n02085620", "n01644900",
+            "n02129165", "n02699494", "n03837869", "n02815834", "n07720875", "n02788148", "n02909870", "n03706229",
+            "n07871810", "n03447447", "n02113799", "n12267677", "n03662601", "n02841315", "n07715103", "n02504458",
+        ]