refactor: all data

.gitattributes

@@ -53,3 +53,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
+data filter=lfs diff=lfs merge=lfs -text

botox-injections-before-and-after.py

@@ -0,0 +1,71 @@
+import datasets
+import pandas as pd
+
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+title = {botox-injections-before-and-after},
+author = {TrainingDataPro},
+year = {2023}
+}
+"""
+
+_DESCRIPTION = """\
+The dataset consists of photos featuring the same individuals captured before and after
+botox injections procedure. The dataset contains a diverse range of individuals with
+various ages, ethnicities and genders.
+The dataset is useful for evaluation of the effectiveness of botox injections for
+different skin and face types, face recognition and reidentification tasks. It can be
+utilised for biometric tasks , in beauty sphere, for medical purposes and e-commerce.
+"""
+_NAME = "botox-injections-before-and-after"
+
+_HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}"
+
+_LICENSE = ""
+
+_DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/"
+
+
+class BotoxInjectionsBeforeAndAfter(datasets.GeneratorBasedBuilder):
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("int32"),
+                    "before": datasets.Image(),
+                    "aftre": datasets.Image(),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        before = dl_manager.download(f"{_DATA}before.tar.gz")
+        after = dl_manager.download(f"{_DATA}after.tar.gz")
+        annotations = dl_manager.download(f"{_DATA}{_NAME}.csv")
+        before = dl_manager.iter_archive(before)
+        after = dl_manager.iter_archive(after)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "before": before,
+                    "after": after,
+                    "annotations": annotations,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, before, after, annotations):
+        for idx, (
+            (before_image_path, before_image),
+            (after_image_path, after_image),
+        ) in enumerate(zip(before, after)):
+            yield idx, {
+                "id": before_image_path.split("/")[-1].split(".")[0],
+                "image": {"path": before_image_path, "bytes": before_image.read()},
+                "mask": {"path": after_image_path, "bytes": after_image.read()},
+            }

after/0.png → data/after.tar.gz

@@ -1,3 +1,3 @@
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-oid sha256:ea80ff34263f96ed8ef8a6ec652b50f2c04ee068a8e2700940ccf4232edb411a
-size 237887
+oid sha256:514f7fc41e53b9b3e168a840f0b71713667c020608cf94d189aa599f23f6f2b7
+size 19463056

after/1.png → data/before.tar.gz

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:780aff3c9e33e5a69971fda2e6a87705a23f73756bab74e2279e73c63fa1be85
-size 209098
+oid sha256:4b605d85f21d666a7e56b1aef29ebf27b7b2ac22d6b38e1c9310a6ccfc17398a
+size 19360475