Update parquet files

.gitattributes

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-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
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-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.lz4 filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
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-*.parquet filter=lfs diff=lfs merge=lfs -text
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-*.pickle filter=lfs diff=lfs merge=lfs -text
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-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tgz filter=lfs diff=lfs merge=lfs -text
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-*tfevents* filter=lfs diff=lfs merge=lfs -text
-# Audio files - uncompressed
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-*.raw filter=lfs diff=lfs merge=lfs -text
-# Audio files - compressed
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-# Image files - uncompressed
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README.md

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----
-language:
-- en
-license:
-- mit
-source_datasets:
-- original
-task_categories:
-- image-segmentation
-- object-detection
-task_ids: []
-tags:
-- optical-character-recognition
-- text-detection
-- ocr
----
-
-
-# School Notebooks Dataset
-
-The images of school notebooks with handwritten notes in English.
-
-The dataset annotation contain end-to-end markup for training detection and OCR models, as well as an end-to-end model for reading text from pages.
-
-## Annotation format
-
-The annotation is in COCO format. The `annotation.json` should have the following dictionaries:
-
-- `annotation["categories"]` - a list of dicts with a categories info (categotiy names and indexes).
-- `annotation["images"]` - a list of dictionaries with a description of images, each dictionary must contain fields:
-   - `file_name` - name of the image file.
-   - `id` for image id.
-- `annotation["annotations"]` - a list of dictioraties with a murkup information. Each dictionary stores a description for one polygon from the dataset, and must contain the following fields:
-   - `image_id` - the index of the image on which the polygon is located.
-   - `category_id` - the polygon’s category index.
-   - `attributes` - dict with some additional annotation information. In the `translation` subdict you can find text translation for the line.
-   - `segmentation` - the coordinates of the polygon, a list of numbers - which are coordinate pairs x and y.

dataset_infos.json

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-{"default": {"description": "", "citation": "", "homepage": "", "license": "", "features": {"image": {"decode": true, "id": null, "_type": "Image"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "school_notebooks_en", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 11957, "num_examples": 70, "dataset_name": "school_notebooks_en"}, "test": {"name": "test", "num_bytes": 1692, "num_examples": 10, "dataset_name": "school_notebooks_en"}, "validation": {"name": "validation", "num_bytes": 1726, "num_examples": 10, "dataset_name": "school_notebooks_en"}}, "download_checksums": {"images.zip": {"num_bytes": 356400252, "checksum": "295ab37246a1ec976f50019ab77b3bb35543b0cdcbd2b825a7949c4959448a75"}, "annotations_train.json": {"num_bytes": 8644229, "checksum": "2b52b1876e47c6970e2ba97f57fe2a431a2bca0c5b9450360c4bf481643c0b5f"}, "annotations_test.json": {"num_bytes": 687288, "checksum": "c241e966d26d10cd7fa19730489cac104ebebfd9931d0acd0c4fb7369a958672"}, "annotations_val.json": {"num_bytes": 1768738, "checksum": "8a6b80a2c73ca25a2b2b23d4620c4c34492b728ed094a824e82f49427712c271"}}, "download_size": 367500507, "post_processing_size": null, "dataset_size": 15375, "size_in_bytes": 367515882}}

images.zip → default/school_notebooks_en-test.parquet

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default/school_notebooks_en-train.parquet

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default/school_notebooks_en-validation.parquet

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school_notebooks_EN.py

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-import os
-import json
-import datasets
-
-
-class SchoolNotebooks(datasets.GeneratorBasedBuilder):
-    def _info(self):
-        return datasets.DatasetInfo(
-            features=datasets.Features(
-                {
-                    "image": datasets.Image(),
-                }
-            )
-        )
-
-    def _split_generators(self, dl_manager):
-        _URLS = {
-            "images": "images.zip",
-            "train_data": "annotations_train.json",
-            "test_data": "annotations_test.json",
-            "val_data": "annotations_val.json"
-        }
-        data_files = dl_manager.download_and_extract(_URLS)
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "image_paths": dl_manager.iter_files(data_files["images"]),
-                    "annotation_path": data_files["train_data"],
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "image_paths": dl_manager.iter_files(data_files["images"]),
-                    "annotation_path": data_files["test_data"],
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "image_paths": dl_manager.iter_files(data_files["images"]),
-                    "annotation_path": data_files["val_data"],
-                },
-            )
-        ]
-
-    def _generate_examples(self, image_paths, annotation_path):
-        """Generate examples."""
-        with open(annotation_path, 'r') as f:
-            data = json.load(f)
-
-        image_names = set()
-        for image_data in data['images']:
-            image_names.add(image_data['file_name'])
-
-        for idx, image_path in enumerate(image_paths):
-            if os.path.basename(image_path) in image_names:
-                example = {
-                    "image": image_path,
-                }
-                yield idx, example