update

.gitignore

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+annotations/
+annotations_trainval2017.zip
+*.jsonl

README.md

@@ -1,3 +1,193 @@
 ---
 license: cc-by-4.0
+size_categories:
+- n<1K
+task_categories:
+- image-to-text
+language:
+- en
+pretty_name: COCO Captions
 ---
+
+# Dataset Card for "COCO 2017"
+
+## Quick Start
+### Usage
+```python
+>>> from datasets.load import load_dataset
+
+>>> dataset = load_dataset('whyen-wang/coco_captions')
+>>> example = dataset['train'][500]
+>>> print(example)
+{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x426>,
+ 'captions': ['A plate that has food on top of it with powdered sugar.',
+  'A breakfast item on a plate is sitting on a table.',
+  'different kinds of food on a glass plate',
+  'a bowl with some pancakes and toppings on it',
+  'Pancakes on a plate with banana, sauce and whipped cream toppings']}
+```
+
+### Visualization
+```python
+>>> import cv2
+>>> import numpy as np
+>>> from PIL import Image
+
+>>> def visualize(example):
+    for caption in example['captions']:
+        print(caption)
+    image = np.array(example['image'])
+    return image
+
+>>> Image.fromarray(example)
+```
+
+## 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/
+- **Repository:** None
+- **Paper:** [Microsoft COCO: Common Objects in Context](https://arxiv.org/abs/1405.0312)
+- **Leaderboard:** [Papers with Code](https://paperswithcode.com/dataset/coco)
+- **Point of Contact:** None
+
+### Dataset Summary
+
+COCO is a large-scale object detection, segmentation, and captioning dataset.
+
+### Supported Tasks and Leaderboards
+
+[Image to Text](https://huggingface.co/tasks/image-to-text)
+
+### Languages
+
+en
+
+## Dataset Structure
+
+### Data Instances
+
+An example looks as follows.
+
+```
+{
+    "image": PIL.Image(mode="RGB"),
+    "captions": [
+        "Closeup of bins of food that include broccoli and bread.",
+        "A meal is presented in brightly colored plastic trays.",
+        "there are containers filled with different kinds of foods",
+        "Colorful dishes holding meat, vegetables, fruit, and bread.",
+        "A bunch of trays that have different food."
+    ]
+}
+```
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+|    name |   train | validation |
+| ------- | ------: | ---------: |
+| default | 118,287 |      5,000 |
+
+## 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
+
+Creative Commons Attribution 4.0 License
+
+### Citation Information
+
+```
+@article{cocodataset,
+  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},
+  archivePrefix = {arXiv},
+  eprint    = {1405.0312},
+  timestamp = {Mon, 13 Aug 2018 16:48:13 +0200},
+  biburl    = {https://dblp.org/rec/bib/journals/corr/LinMBHPRDZ14},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+```
+
+### Contributions
+
+Thanks to [@github-whyen-wang](https://github.com/whyen-wang) for adding this dataset.

coco_captions.py

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+import json
+import datasets
+from pathlib import Path
+
+_HOMEPAGE = 'https://cocodataset.org/'
+_LICENSE = 'Creative Commons Attribution 4.0 License'
+_DESCRIPTION = 'COCO is a large-scale object detection, segmentation, and captioning dataset.'
+_CITATION = '''\
+@article{cocodataset,
+  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},
+  archivePrefix = {arXiv},
+  eprint    = {1405.0312},
+  timestamp = {Mon, 13 Aug 2018 16:48:13 +0200},
+  biburl    = {https://dblp.org/rec/bib/journals/corr/LinMBHPRDZ14},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+'''
+
+
+class COCOCaptionsConfig(datasets.BuilderConfig):
+    '''Builder Config for coco2017'''
+
+    def __init__(
+        self, description, homepage,
+        annotation_urls, **kwargs
+    ):
+        super(COCOCaptionsConfig, self).__init__(
+            version=datasets.Version('1.0.0', ''),
+            **kwargs
+        )
+        self.description = description
+        self.homepage = homepage
+        url = 'http://images.cocodataset.org/zips/'
+        self.train_image_url = url + 'train2017.zip'
+        self.val_image_url = url + 'val2017.zip'
+        self.annotation_urls = annotation_urls
+
+
+class COCOCaptions(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        COCOCaptionsConfig(
+            description=_DESCRIPTION,
+            homepage=_HOMEPAGE,
+            annotation_urls={
+                'train': 'data/captions_train.jsonl',
+                'validation': 'data/captions_validation.jsonl'
+            },
+        )
+    ]
+
+    def _info(self):
+        features = datasets.Features({
+            'image': datasets.Image(mode='RGB', decode=True, id=None),
+            'captions': datasets.Sequence(
+                feature=datasets.Value(dtype='string', id=None),
+                length=-1, id=None
+            )
+        })
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION
+        )
+
+    def _split_generators(self, dl_manager):
+        train_image_path = dl_manager.download_and_extract(
+            self.config.train_image_url
+        )
+        validation_image_path = dl_manager.download_and_extract(
+            self.config.val_image_url
+        )
+        annotation_paths = dl_manager.download_and_extract(
+            self.config.annotation_urls
+        )
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    'image_path': f'{train_image_path}/train2017',
+                    'annotation_path': annotation_paths['train']
+                }
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    'image_path': f'{validation_image_path}/val2017',
+                    'annotation_path': annotation_paths['validation']
+                }
+            )
+        ]
+
+    def _generate_examples(self, image_path, annotation_path):
+        idx = 0
+        image_path = Path(image_path)
+        with open(annotation_path, 'r', encoding='utf-8') as f:
+            for line in f:
+                obj = json.loads(line.strip())
+                example = {
+                    'image': str(image_path / obj['image']),
+                    'captions': obj['captions']
+                }
+                yield idx, example
+                idx += 1

data/captions_train.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:14b9a0fc5c5c1de4f85c186756fa16eb8f20c7fd39ad620868d977e932240fe1
+size 10150166

data/captions_validation.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fb39c6b91b7362e386f29e085274080b519bd8eeb67c7c3d5aa28e240422c2b6
+size 456607

prepare.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import zipfile\n",
+    "import requests\n",
+    "import jsonlines\n",
+    "from tqdm import tqdm\n",
+    "from pathlib import Path\n",
+    "from pycocotools.coco import COCO\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Download Annotations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "url = 'http://images.cocodataset.org/annotations/'\n",
+    "file = 'annotations_trainval2017.zip'\n",
+    "if not Path(f'./{file}').exists():\n",
+    "    response = requests.get(url + file)\n",
+    "    with open(file, 'wb') as f:\n",
+    "        f.write(response.content)\n",
+    "\n",
+    "    with zipfile.ZipFile(file, 'r') as zipf:\n",
+    "        zipf.extractall(Path())\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Read annotations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coco91_to_coco80 = [\n",
+    "    None, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, None,\n",
+    "    11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22,\n",
+    "    23, None, 24, 25, None, None, 26, 27, 28, 29, 30,\n",
+    "    31, 32, 33, 34, 35, 36, 37, 38, 39, None, 40, 41,\n",
+    "    42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,\n",
+    "    55, 56, 57, 58, 59, None, 60, None, None, 61, None,\n",
+    "    62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, None,\n",
+    "    73, 74, 75, 76, 77, 78, 79\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Image Captioning Task"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_data = COCO('annotations/captions_train2017.json')\n",
+    "val_data = COCO('annotations/captions_val2017.json')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for split, data in zip(['train', 'validation'], [train_data, val_data]):\n",
+    "    with jsonlines.open(f'data/captions_{split}.jsonl', mode='w') as writer:\n",
+    "        for image_id, image_info in tqdm(data.imgs.items()):\n",
+    "            captions = []\n",
+    "            anns = data.imgToAnns[image_id]\n",
+    "            for ann in anns:\n",
+    "                captions.append(ann['caption'])\n",
+    "            writer.write({\n",
+    "                'image': image_info['file_name'], 'captions': captions\n",
+    "            })"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for split in ['train', 'validation']:\n",
+    "    file_path = f'data/captions_{split}.jsonl'\n",
+    "    with zipfile.ZipFile(f'data/captions_{split}.zip', 'w', zipfile.ZIP_DEFLATED) as zipf:\n",
+    "        zipf.write(file_path, os.path.basename(file_path))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}