coco dataset - 2014 subset with karpathy annotations/splits

COCO.py

@@ -0,0 +1,176 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""COCO"""
+import json
+import os
+from pathlib import Path
+
+import datasets
+
+
+_CITATION = """
+@article{DBLP:journals/corr/LinMBHPRDZ14,
+  author    = {Tsung{-}Yi Lin and
+               Michael Maire and
+               Serge J. Belongie and
+               Lubomir D. Bourdev and
+               Ross B. Girshick and
+               James Hays and
+               Pietro Perona and
+               Deva Ramanan and
+               Piotr Doll{\'{a}}r and
+               C. Lawrence Zitnick},
+  title     = {Microsoft {COCO:} Common Objects in Context},
+  journal   = {CoRR},
+  volume    = {abs/1405.0312},
+  year      = {2014},
+  url       = {http://arxiv.org/abs/1405.0312},
+  eprinttype = {arXiv},
+  eprint    = {1405.0312},
+  timestamp = {Mon, 13 Aug 2018 16:48:13 +0200},
+  biburl    = {https://dblp.org/rec/journals/corr/LinMBHPRDZ14.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+"""
+
+_DESCRIPTION = """
+MS COCO is a large-scale object detection, segmentation, and captioning dataset.
+COCO has several features: Object segmentation, Recognition in context, Superpixel stuff segmentation, 330K images (>200K labeled), 1.5 million object instances, 80 object categories, 91 stuff categories, 5 captions per image, 250,000 people with keypoints.
+"""
+
+_HOMEPAGE = "https://cocodataset.org/#home"
+
+_LICENSE = "CC BY 4.0"
+
+
+_IMAGES_URLS = {
+    "train": "http://images.cocodataset.org/zips/train2014.zip",
+    "validation": "http://images.cocodataset.org/zips/val2014.zip",
+}
+
+_KARPATHY_FILES_URL = "https://cs.stanford.edu/people/karpathy/deepimagesent/caption_datasets.zip"
+
+_SPLIT_MAP = {"train": "train2014", "validation": "val2014"}
+
+_FEATURES = datasets.Features(
+    {
+        "image": datasets.Image(),
+        "filepath": datasets.Value("string"),
+        "sentids": [datasets.Value("int32")],
+        "filename": datasets.Value("string"),
+        "imgid": datasets.Value("int32"),
+        "split": datasets.Value("string"),
+        "sentences": {
+            "tokens": [datasets.Value("string")],
+            "raw": datasets.Value("string"),
+            "imgid": datasets.Value("int32"),
+            "sentid": datasets.Value("int32"),
+        },
+        "cocoid": datasets.Value("int32"),
+    }
+)
+
+
+class COCO(datasets.GeneratorBasedBuilder):
+    """COCO"""
+
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="2014", version=VERSION, description="2014 version of COCO with Karpathy annotations and splits"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "2014"
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=_FEATURES,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        annotation_file = os.path.join(dl_manager.download_and_extract(_KARPATHY_FILES_URL), "dataset_coco.json")
+        image_folders = {k: Path(v) for k, v in dl_manager.download_and_extract(_IMAGES_URLS).items()}
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "annotation_file": annotation_file,
+                    "image_folders": image_folders,
+                    "split_key": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "annotation_file": annotation_file,
+                    "image_folders": image_folders,
+                    "split_key": "validation",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "annotation_file": annotation_file,
+                    "image_folders": image_folders,
+                    "split_key": "test",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, annotation_file, image_folders, split_key):
+        counter = 0
+        with open(annotation_file, "r", encoding="utf-8") as fi:
+            annotations = json.load(fi)
+
+            for image_metadata in annotations["images"]:
+                if split_key == "train":
+                    if image_metadata["split"] != "train" and image_metadata["split"] != "restval":
+                        continue
+                elif split_key == "validation":
+                    if image_metadata["split"] != "val":
+                        continue
+                elif split_key == "test":
+                    if image_metadata["split"] != "test":
+                        continue
+
+                if "val2014" in image_metadata["filename"]:
+                    image_path = image_folders["validation"] / _SPLIT_MAP["validation"]
+                else:
+                    image_path = image_folders["train"] / _SPLIT_MAP["train"]
+
+                image_path = image_path / image_metadata["filename"]
+
+                for caption in image_metadata["sentences"]:
+                    yield counter, {
+                        "image": str(image_path.absolute()),
+                        "filepath": image_metadata["filename"],
+                        "sentids": image_metadata["sentids"],
+                        "filename": image_metadata["filename"],
+                        "imgid": image_metadata["imgid"],
+                        "split": image_metadata["split"],
+                        "sentences": {
+                            "tokens": caption["tokens"],
+                            "raw": caption["raw"],
+                            "imgid": caption["imgid"],
+                            "sentid": caption["sentid"],
+                        },
+                        "cocoid": image_metadata["cocoid"],
+                    }
+                    counter += 1

README.md

@@ -1,3 +1,146 @@
 ---
 license: cc-by-4.0
 ---
+
+# Dataset Card for [Dataset Name]
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** [https://cocodataset.org/](https://cocodataset.org/)
+- **Repository:**
+- **Paper:** [Microsoft COCO: Common Objects in Context](https://arxiv.org/abs/1405.0312)
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+MS COCO is a large-scale object detection, segmentation, and captioning dataset.
+COCO has several features: Object segmentation, Recognition in context, Superpixel stuff segmentation, 330K images (>200K labeled), 1.5 million object instances, 80 object categories, 91 stuff categories, 5 captions per image, 250,000 people with keypoints.
+
+As of now, there is only the 2014 subset (with Karpathy annotations and splits), but feel free to contribute the 2017 subset of COCO!
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+[More Information Needed]
+
+## Dataset Structure
+
+### Data Instances
+
+Each instance has the following structure:
+```
+{
+    'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x480 at 0x7F69C1BA8550>,
+    'filepath': 'COCO_val2014_000000522418.jpg',
+    'sentids': [681330, 686718, 688839, 693159, 693204],
+    'filename': 'COCO_val2014_000000522418.jpg',
+    'imgid': 1,
+    'split': 'restval',
+    'sentences': {
+        'tokens': ['a', 'woman', 'wearing', 'a', 'net', 'on', 'her', 'head', 'cutting', 'a', 'cake'],
+        'raw': 'A woman wearing a net on her head cutting a cake. ',
+        'imgid': 1,
+        'sentid': 681330
+    },
+    'cocoid': 522418
+}
+```
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+[More Information Needed]
+
+### Contributions
+
+Thanks to [@VictorSanh](https://github.com/VictorSanh) for adding this dataset.