Create tiny-imagenet.py

tiny-imagenet.py

@@ -0,0 +1,93 @@
+import os
+import datasets
+
+
+class TinyImagenet(datasets.GeneratorBasedBuilder):
+    """
+    Tiny ImageNet dataset for image classification tasks.
+    It contains 200 classes with 500 training images, 50 validation images, and 50 test images per class.
+    """
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description="""In Tiny ImageNet, there are 100,000 images divided up into 200 classes. Every image in the 
+            dataset is downsized to a 64×64 colored image. For every class, there are 500 training images, 50 validating 
+            images, and 50 test images.""",
+            features=datasets.Features(
+                {
+                    "image": datasets.Image(),
+                    "label": datasets.ClassLabel(names=self._labels())
+                }
+            ),
+            supervised_keys=("image", "label"),
+            homepage="https://www.kaggle.com/c/tiny-imagenet",
+            citation="""@inproceedings{Le2015TinyIV,
+                          title={Tiny ImageNet Visual Recognition Challenge},
+                          author={Ya Le and Xuan S. Yang},
+                          year={2015}
+                        }""",
+            license="MIT License",
+        )
+
+    def _split_generators(self, dl_manager):
+        url = "http://cs231n.stanford.edu/tiny-imagenet-200.zip"
+        archive_path = dl_manager.download_and_extract(url)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"archive_path": archive_path, "split": "train"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"archive_path": archive_path, "split": "val"},
+            ),
+        ]
+
+    def _generate_examples(self, archive_path, split):
+        base_path = os.path.join(archive_path, "tiny-imagenet-200")
+
+        if split == "train":
+            for label_dir in os.listdir(os.path.join(base_path, "train")):
+                class_path = os.path.join(base_path, "train", label_dir, "images")
+                for image_file in os.listdir(class_path):
+                    image_path = os.path.join(class_path, image_file)
+                    yield image_file, {"image": image_path, "label": label_dir}
+
+        elif split == "val":
+            annotations = os.path.join(base_path, "val", "val_annotations.txt")
+            with open(annotations, "r") as f:
+                for line in f:
+                    image_file, label, *_ = line.strip().split("\t")
+                    image_path = os.path.join(base_path, "val", "images", image_file)
+                    yield image_file, {"image": image_path, "label": label}
+
+    @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']