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README.md

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+---
+dataset_info:
+  features:
+  - name: image
+    dtype: image
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': red and white circle 20 kph speed limit
+          '1': red and white circle 30 kph speed limit
+          '2': red and white circle 50 kph speed limit
+          '3': red and white circle 60 kph speed limit
+          '4': red and white circle 70 kph speed limit
+          '5': red and white circle 80 kph speed limit
+          '6': end / de-restriction of 80 kph speed limit
+          '7': red and white circle 100 kph speed limit
+          '8': red and white circle 120 kph speed limit
+          '9': red and white circle red car and black car no passing
+          '10': red and white circle red truck and black car no passing
+          '11': red and white triangle road intersection warning
+          '12': white and yellow diamond priority road
+          '13': red and white upside down triangle yield right-of-way
+          '14': stop
+          '15': empty red and white circle
+          '16': red and white circle no truck entry
+          '17': red circle with white horizonal stripe no entry
+          '18': red and white triangle with exclamation mark warning
+          '19': red and white triangle with black left curve approaching warning
+          '20': red and white triangle with black right curve approaching warning
+          '21': red and white triangle with black double curve approaching warning
+          '22': red and white triangle rough / bumpy road warning
+          '23': red and white triangle car skidding / slipping warning
+          '24': red and white triangle with merging / narrow lanes warning
+          '25': red and white triangle with person digging / construction / road work
+            warning
+          '26': red and white triangle with traffic light approaching warning
+          '27': red and white triangle with person walking warning
+          '28': red and white triangle with child and person walking warning
+          '29': red and white triangle with bicyle warning
+          '30': red and white triangle with snowflake / ice warning
+          '31': red and white triangle with deer warning
+          '32': white circle with gray strike bar no speed limit
+          '33': blue circle with white right turn arrow mandatory
+          '34': blue circle with white left turn arrow mandatory
+          '35': blue circle with white forward arrow mandatory
+          '36': blue circle with white forward or right turn arrow mandatory
+          '37': blue circle with white forward or left turn arrow mandatory
+          '38': blue circle with white keep right arrow mandatory
+          '39': blue circle with white keep left arrow mandatory
+          '40': blue circle with white arrows indicating a traffic circle
+          '41': white circle with gray strike bar indicating no passing for cars has
+            ended
+          '42': white circle with gray strike bar indicating no passing for trucks
+            has ended
+  splits:
+  - name: train
+    num_bytes: 3190230
+    num_examples: 26640
+  - name: test
+    num_bytes: 1411339
+    num_examples: 12630
+  - name: contrast
+    num_bytes: 1512379
+    num_examples: 12630
+  - name: gaussian_noise
+    num_bytes: 1663939
+    num_examples: 12630
+  - name: impulse_noise
+    num_bytes: 1638679
+    num_examples: 12630
+  - name: jpeg_compression
+    num_bytes: 1714459
+    num_examples: 12630
+  - name: motion_blur
+    num_bytes: 1588159
+    num_examples: 12630
+  - name: pixelate
+    num_bytes: 1512379
+    num_examples: 12630
+  - name: spatter
+    num_bytes: 1487119
+    num_examples: 12630
+  download_size: 841108239
+  dataset_size: 15718682
+---

data/contrast.zip

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+oid sha256:66d8de328ac8e6194b8e445a7f302570a371f5b22a13c7840f818facd725f498
+size 72731662

data/gaussian_noise.zip

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+size 107066840

data/impulse_noise.zip

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+size 90390821

data/jpeg_compression.zip

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+size 86363380

data/motion_blur.zip

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+size 88657131

data/pixelate.zip

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+size 27164607

data/spatter.zip

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+size 91524208

data/test.zip

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+size 89209187

data/train.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:97793acfe2ccddfa78c52e85d2f5344d68b0b14fae17ea6976660975a21dfdc0
+size 188000403

gtsrb.py

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+import datasets
+from datasets.data_files import DataFilesDict
+from datasets.packaged_modules.imagefolder.imagefolder import ImageFolder, ImageFolderConfig
+
+logger = datasets.logging.get_logger(__name__)
+
+
+class GTSRB(ImageFolder):
+    R"""
+    GTSRB dataset for image classification.
+    """
+
+    BUILDER_CONFIG_CLASS = ImageFolderConfig
+    BUILDER_CONFIGS = [
+        ImageFolderConfig(name='default', features=("images", "labels"), data_files=DataFilesDict({split: f"data/{split}.zip" for split in ["train", "test"] + ["contrast", "gaussian_noise", "impulse_noise", "jpeg_compression", "motion_blur", "pixelate", "spatter"]}),)
+    ]
+
+
+    classnames = [
+        "red and white circle 20 kph speed limit",
+        "red and white circle 30 kph speed limit",
+        "red and white circle 50 kph speed limit",
+        "red and white circle 60 kph speed limit",
+        "red and white circle 70 kph speed limit",
+        "red and white circle 80 kph speed limit",
+        "end / de-restriction of 80 kph speed limit",
+        "red and white circle 100 kph speed limit",
+        "red and white circle 120 kph speed limit",
+        "red and white circle red car and black car no passing",
+        "red and white circle red truck and black car no passing",
+        "red and white triangle road intersection warning",
+        "white and yellow diamond priority road",
+        "red and white upside down triangle yield right-of-way",
+        "stop",
+        "empty red and white circle",
+        "red and white circle no truck entry",
+        "red circle with white horizonal stripe no entry",
+        "red and white triangle with exclamation mark warning",
+        "red and white triangle with black left curve approaching warning",
+        "red and white triangle with black right curve approaching warning",
+        "red and white triangle with black double curve approaching warning",
+        "red and white triangle rough / bumpy road warning",
+        "red and white triangle car skidding / slipping warning",
+        "red and white triangle with merging / narrow lanes warning",
+        "red and white triangle with person digging / construction / road work warning",
+        "red and white triangle with traffic light approaching warning",
+        "red and white triangle with person walking warning",
+        "red and white triangle with child and person walking warning",
+        "red and white triangle with bicyle warning",
+        "red and white triangle with snowflake / ice warning",
+        "red and white triangle with deer warning",
+        "white circle with gray strike bar no speed limit",
+        "blue circle with white right turn arrow mandatory",
+        "blue circle with white left turn arrow mandatory",
+        "blue circle with white forward arrow mandatory",
+        "blue circle with white forward or right turn arrow mandatory",
+        "blue circle with white forward or left turn arrow mandatory",
+        "blue circle with white keep right arrow mandatory",
+        "blue circle with white keep left arrow mandatory",
+        "blue circle with white arrows indicating a traffic circle",
+        "white circle with gray strike bar indicating no passing for cars has ended",
+        "white circle with gray strike bar indicating no passing for trucks has ended",
+    ]
+
+    clip_templates = [
+        lambda c: f'a zoomed in photo of a "{c}" traffic sign.',
+        lambda c: f'a centered photo of a "{c}" traffic sign.',
+        lambda c: f'a close up photo of a "{c}" traffic sign.',
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description="GTSRB dataset for image classification.",
+            features=datasets.Features(
+                {
+                    "image": datasets.Image(),
+                    "label": datasets.ClassLabel(names=self.classnames),
+                }
+            ),
+            supervised_keys=("image", "label"),
+            task_templates=[datasets.ImageClassification(image_column="image", label_column="label")],
+        )
+