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

data/generated-vietnamese-passeports-dataset.csv

@@ -0,0 +1,21 @@
+image_id,image
+0,images/0.png
+1,images/1.png
+2,images/2.png
+3,images/3.png
+4,images/4.png
+5,images/5.png
+6,images/6.png
+7,images/7.png
+8,images/8.png
+9,images/9.png
+10,images/10.png
+11,images/11.png
+12,images/12.png
+13,images/13.png
+14,images/14.png
+15,images/15.png
+16,images/16.png
+17,images/17.png
+18,images/18.png
+19,images/19.png

images/0.png → data/images.tar.gz

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:e4abcd0ebd969217397ffbccb40e2e26f8c6463ac3cbfd74118a69e20e54ba05
-size 1629996
+oid sha256:58db7f24fdf48a252f1bc347fccad939bee6a1630b4822d4daef4858109aabbb
+size 28741603

generated-vietnamese-passeports-dataset.py

@@ -0,0 +1,74 @@
+import datasets
+import pandas as pd
+
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+title = {generated-vietnamese-passeports-dataset},
+author = {TrainingDataPro},
+year = {2023}
+}
+"""
+
+_DESCRIPTION = """\
+Data generation in machine learning involves creating or manipulating data to train
+and evaluate machine learning models. The purpose of data generation is to provide
+diverse and representative examples that cover a wide range of scenarios, ensuring the
+model's robustness and generalization.
+The dataset contains GENERATED Vietnamese passports, which are replicas of official
+passports but with randomly generated details, such as name, date of birth etc.
+The primary intention of generating these fake passports is to demonstrate the
+structure and content of a typical passport document and to train the neural network to
+identify this type of document.
+Generated passports can assist in conducting research without accessing or compromising
+real user data that is often sensitive and subject to privacy regulations. Synthetic
+data generation allows researchers to *develop and refine models using simulated
+passport data without risking privacy leaks*.
+"""
+_NAME = "generated-vietnamese-passeports-dataset"
+
+_HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}"
+
+_LICENSE = ""
+
+_DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/"
+
+
+class GeneratedVietnamesePasseportsDataset(datasets.GeneratorBasedBuilder):
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("int32"),
+                    "image": datasets.Image()
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        images = dl_manager.download(f"{_DATA}images.tar.gz")
+        annotations = dl_manager.download(f"{_DATA}{_NAME}.csv")
+        images = dl_manager.iter_archive(images)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "images": images,
+                    "annotations": annotations,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, images, masks, annotations):
+        annotations_df = pd.read_csv(annotations)
+
+        for idx, (image_path, image) in enumerate(
+            zip(images, masks)
+        ):
+            yield idx, {
+                "id": annotations_df["image_id"].iloc[idx],
+                "image": {"path": image_path, "bytes": image.read()}
+            }

passports.csv

@@ -1,21 +0,0 @@
-image
-images/0.png
-images/1.png
-images/2.png
-images/3.png
-images/4.png
-images/5.png
-images/6.png
-images/7.png
-images/8.png
-images/9.png
-images/10.png
-images/11.png
-images/12.png
-images/13.png
-images/14.png
-images/15.png
-images/16.png
-images/17.png
-images/18.png
-images/19.png