create dataset builder file

shrek-detection.py

@@ -0,0 +1,60 @@
+import datasets
+
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+title = {Shrek Detection Dataset},
+author={Aurelio AI},
+year={2024}
+}
+"""
+
+_DESCRIPTION = """\
+Demo dataset for testing Shrek detection capabilities in images.
+"""
+_HOMEPAGE = "https://huggingface.co/datasets/aurelio-ai/shrek-detection"
+
+_LICENSE = ""
+
+_REPO = "https://huggingface.co/datasets/aurelio-ai/shrek-detection"
+
+class ImageSet(datasets.GeneratorBasedBuilder):
+    """Small sample of image-text pairs"""
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    'text': datasets.Value("string"),
+                    'image': datasets.Image(),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        images_archive = dl_manager.download(f"{_REPO}/resolve/main/images.tgz")
+        image_iters = dl_manager.iter_archive(images_archive)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "images": image_iters
+                }
+            ),
+        ]
+
+    def _generate_examples(self, images):
+        """ This function returns the examples in the raw (text) form."""
+        
+        for idx, (filepath, image) in enumerate(images):
+            filename = filepath.split('/')[-1][:-4]
+            cls = int(filename)[0]
+            description = filename[1:].replace('_', ' ')
+            yield idx, {
+                "image": {"path": filepath, "bytes": image.read()},
+                "text": description,
+                "is_shrek": cls
+            }