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

images/01.jpg → data/images.tar.gz

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:2a670147613b0dc01ac657ea0584e6cd7d2e7f15ee70d5906dfa9d7424ba22e0
-size 433226
+oid sha256:57989232c87442f803da5e5123735f6f748b97b8c83670f9a8bb79ca72fa6bf2
+size 3027445

data/ocr-trains-dataset.csv

@@ -0,0 +1,14 @@
+image_id,image_name,annotations
+1,01.jpg,"[{'type': 'box', 'label': 'numbers', 'points': [(1655.38, 519.7), (1851.68, 561.4)], 'attributes': [{'name': 'text', 'text': '61640132'}]}]"
+2,02.jpg,"[{'type': 'box', 'label': 'numbers', 'points': [(248.8, 241.23), (352.9, 282.22)], 'attributes': [{'name': 'text', 'text': '55638167'}]}]"
+3,03.jpg,"[{'type': 'box', 'label': 'numbers', 'points': [(504.4, 221.5), (600.4, 240.4)], 'attributes': [{'name': 'text', 'text': '95095857'}]}]"
+4,04.jpg,"[{'type': 'box', 'label': 'numbers', 'points': [(719.93, 185.34), (828.27, 229.45)], 'attributes': [{'name': 'text', 'text': '63164818'}]}]"
+5,05.jpg,"[{'type': 'box', 'label': 'numbers', 'points': [(785.2, 240.1), (1010.1, 284.7)], 'attributes': [{'name': 'text', 'text': '63673149'}]}]"
+6,06.jpg,"[{'type': 'box', 'label': 'numbers', 'points': [(483.47, 286.6), (612.27, 322.83)], 'attributes': [{'name': 'text', 'text': '60517067'}]}]"
+7,07.jpg,"[{'type': 'box', 'label': 'numbers', 'points': [(295.5, 93.89), (438.09, 133.59)], 'attributes': [{'name': 'text', 'text': '62071246'}]}]"
+8,08.jpg,"[{'type': 'box', 'label': 'numbers', 'points': [(218.49, 14.1), (328.19, 48.68)], 'attributes': [{'name': 'text', 'text': '52605870'}]}]"
+9,09.jpg,"[{'type': 'box', 'label': 'numbers', 'points': [(142.71, 257.5), (235.2, 276.4)], 'attributes': [{'name': 'text', 'text': '95500344'}]}]"
+10,10.jpg,"[{'type': 'box', 'label': 'numbers', 'points': [(189.29, 56.4), (261.72, 78.7)], 'attributes': [{'name': 'text', 'text': '61677258'}]}]"
+11,11.jpg,"[{'type': 'box', 'label': 'numbers', 'points': [(389.2, 141.66), (482.4, 162.4)], 'attributes': [{'name': 'text', 'text': '42092288'}]}]"
+12,12.jpg,"[{'type': 'box', 'label': 'numbers', 'points': [(132.8, 164.7), (226.1, 183.8)], 'attributes': [{'name': 'text', 'text': '50915024'}]}]"
+13,13.jpg,"[{'type': 'box', 'label': 'numbers', 'points': [(495.2, 208.11), (618.1, 234.79)], 'attributes': [{'name': 'text', 'text': '68965877'}]}]"

ocr-trains-dataset.py

@@ -0,0 +1,72 @@
+import datasets
+import pandas as pd
+
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+title = {ocr-trains-dataset},
+author = {TrainingDataPro},
+year = {2023}
+}
+"""
+
+_DESCRIPTION = """\
+The dataset is a collection of images along with corresponding bounding box annotations
+that are specifically curated for **detecting pigs' heads** in images. The dataset
+covers different *pig breeds, sizes, and orientations*, providing a comprehensive
+representation of pig appearances.
+The pig detection dataset provides a valuable resource for researchers working on pig
+detection tasks. It offers a diverse collection of annotated images, allowing for
+comprehensive algorithm development, evaluation, and benchmarking, ultimately aiding in
+the development of accurate and robust models.
+"""
+_NAME = "ocr-trains-dataset"
+
+_HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}"
+
+_LICENSE = ""
+
+_DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/"
+
+
+class OcrTrainsDataset(datasets.GeneratorBasedBuilder):
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("int32"),
+                    "image": datasets.Image(),
+                    "bboxes": datasets.Value("string"),
+                }
+            ),
+            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,
+                    "masks": masks,
+                    "annotations": annotations,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, images, annotations):
+        annotations_df = pd.read_csv(annotations)
+
+        for idx, (image_path, image) in enumerate(images):
+            yield idx, {
+                "id": annotations_df.loc[annotations_df["image_name"] == image_path][
+                    "image_id"
+                ].values[0],
+                "image": {"path": image_path, "bytes": image.read()},
+                "bboxes": annotations_df["annotations"].iloc[idx],
+            }