commit files to HF hub

README.md

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+
+---
+task_categories:
+- other
+task_ids:
+- other-image-classification
+- image-classification
+tags:
+- auto-generated
+- image-classification
+---
+
+# nateraw/auto-exp-2
+
+Image Classification Dataset
+
+## Usage
+
+```python
+from PIL import Image
+from datasets import load_dataset
+
+def pil_loader(path: str):
+    with open(path, 'rb') as f:
+        im = Image.open(f)
+        return im.convert('RGB')
+
+def image_loader(example_batch):
+    example_batch['image'] = [
+        pil_loader(f) for f in example_batch['file']
+    ]
+    return example_batch
+
+
+ds = load_dataset('nateraw/auto-exp-2')
+ds = ds.with_transform(image_loader)
+```
+

auto-exp-2.py

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+import requests
+from pathlib import Path
+from typing import List
+import datasets
+
+logger = datasets.logging.get_logger(__name__)
+
+_URLS = {'train': 'https://huggingface.co/datasets/auto-exp-2/resolve/main/train.tar.gz'}
+_NAMES = ['cls_a', 'cls_b']
+
+class ImageFolder(datasets.GeneratorBasedBuilder):
+    def _info(self):
+        return datasets.DatasetInfo(
+            features=datasets.Features(
+                dict(
+                    file=datasets.Value("string"),
+                    labels=datasets.features.ClassLabel(names=_NAMES)
+                )
+            ),
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+
+        data_files = dl_manager.download_and_extract(_URLS)
+        if isinstance(data_files, str):
+            return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs=dict(archive_path=data_files))]
+
+        splits = []
+        for split_name, folder in data_files.items():
+            splits.append(datasets.SplitGenerator(name=split_name, gen_kwargs=dict(archive_path=folder)))
+
+        return splits
+
+    def _generate_examples(self, archive_path):
+        labels = self.info.features['labels']
+        extensions = set(('.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif', '.tiff', '.webp'))
+        for i, path in enumerate(Path(archive_path).glob('**/*')):
+            if path.suffix in extensions:
+                yield i, dict(file=path.as_posix(), labels=labels.encode_example(path.parent.name.lower()))

train.tar.gz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:96cabcb8df2e79fc2f45e339dc20b8817dafe04134f5ef1c2c2a51609e40cd90
+size 35475