Duplicate from google/imageinwords

Co-authored-by: Roopal Garg <roopalgarg@users.noreply.huggingface.co>

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README.md

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+---
+annotations_creators:
+- expert-generated
+- crowdsourced
+license: cc-by-4.0
+task_categories:
+- image-to-text
+- text-to-image
+- object-detection
+language:
+- en
+size_categories:
+- 1K<n<10K
+tags:
+- iiw
+- imageinwords
+- image-descriptions
+- image-captions
+- detailed-descriptions
+- hyper-detailed-descriptions
+- object-descriptions
+- object-detection
+- object-labels
+- image-text
+- t2i
+- i2t
+- dataset
+pretty_name: ImageInWords
+multilinguality:
+- monolingual
+---
+
+<h2>ImageInWords: Unlocking Hyper-Detailed Image Descriptions</h2> 
+
+Please visit the [webpage](https://google.github.io/imageinwords) for all the information about the IIW project, data downloads, visualizations, and much more.
+
+<img src="https://github.com/google/imageinwords/blob/main/static/images/Abstract/1_white_background.png?raw=true">
+<img src="https://github.com/google/imageinwords/blob/main/static/images/Abstract/2_white_background.png?raw=true">
+
+Please reach out to iiw-dataset@google.com for thoughts/feedback/questions/collaborations.
+
+<h3>&#129303;Hugging Face&#129303;</h3>
+
+<li><a href="https://huggingface.co/datasets/google/imageinwords">IIW-Benchmark Eval Dataset</a></li>
+
+```python
+from datasets import load_dataset
+
+# `name` can be one of: IIW-400, DCI_Test, DOCCI_Test, CM_3600, LocNar_Eval
+# refer: https://github.com/google/imageinwords/tree/main/datasets
+dataset = load_dataset('google/imageinwords', token=None, name="IIW-400", trust_remote_code=True)
+```
+
+<li><a href="https://huggingface.co/spaces/google/imageinwords-explorer">Dataset-Explorer</a></li>
+
+
+## Dataset Description
+
+- **Paper:** [arXiv](https://arxiv.org/abs/2405.02793)
+- **Homepage:** https://google.github.io/imageinwords/
+- **Point of Contact:** iiw-dataset@google.com
+- **Dataset Explorer:** [ImageInWords-Explorer](https://huggingface.co/spaces/google/imageinwords-explorer)
+
+### Dataset Summary
+
+ImageInWords (IIW), a carefully designed human-in-the-loop annotation framework for curating hyper-detailed image descriptions and a new dataset resulting from this process.
+We validate the framework through evaluations focused on the quality of the dataset and its utility for fine-tuning with considerations for readability, comprehensiveness, specificity, hallucinations, and human-likeness.
+
+This Data Card describes **IIW-Benchmark: Eval Datasets**, a mixture of human annotated and machine generated data intended to help create and capture rich, hyper-detailed image descriptions. 
+
+IIW dataset has two parts: human annotations and model outputs. The main purposes of this dataset are:
+  1) to provide samples from SoTA human authored outputs to promote discussion on annotation guidelines to further improve the quality
+  2) to provide human SxS results and model outputs to promote development of automatic metrics to mimic human SxS judgements.
+
+### Supported Tasks
+
+Text-to-Image, Image-to-Text, Object Detection
+
+### Languages
+
+English
+
+## Dataset Structure
+
+### Data Instances
+
+### Data Fields
+
+For details on the datasets and output keys, please refer to our [GitHub data](https://github.com/google/imageinwords/tree/main/datasets) page inside the individual folders.
+
+IIW-400:
+ - `image/key`
+ - `image/url`
+ - `IIW`: Human generated image description
+ - `IIW-P5B`: Machine generated image description
+ - `iiw-human-sxs-gpt4v` and `iiw-human-sxs-iiw-p5b`: human SxS metrics
+   - metrics/Comprehensiveness
+   - metrics/Specificity
+   - metrics/Hallucination
+   - metrics/First few line(s) as tldr
+   - metrics/Human Like
+
+DCI_Test: 
+ - `image`
+ - `image/url`
+ - `ex_id`
+ - `IIW`: Human authored image description
+ - `metrics/Comprehensiveness`
+ - `metrics/Specificity`
+ - `metrics/Hallucination`
+ - `metrics/First few line(s) as tldr`
+ - `metrics/Human Like`
+
+DOCCI_Test:
+ - `image`
+ - `image/thumbnail_url`
+ - `IIW`: Human generated image description
+ - `DOCCI`: Image description from DOCCI
+ - `metrics/Comprehensiveness`
+ - `metrics/Specificity`
+ - `metrics/Hallucination`
+ - `metrics/First few line(s) as tldr`
+ - `metrics/Human Like`
+
+LocNar_Eval:
+ - `image/key`
+ - `image/url`
+ - `IIW-P5B`: Machine generated image description
+
+CM_3600:
+ - `image/key`
+ - `image/url`
+ - `IIW-P5B`: Machine generated image description
+
+Please note that all fields are string.
+
+### Data Splits
+
+Dataset | Size
+---| ---: 
+IIW-400     | 400
+DCI_Test | 112
+DOCCI_Test    | 100
+LocNar_Eval    | 1000
+CM_3600    | 1000
+
+### Annotations
+
+#### Annotation process
+
+Some text descriptions were written by human annotators and some were generated by machine models.
+The metrics are all from human SxS.
+
+### Personal and Sensitive Information
+
+The images that were used for the descriptions and the machine generated text descriptions are checked (by algorithmic methods and manual inspection) for S/PII, pornographic content, and violence and any we found may contain such information have been filtered. 
+We asked that human annotators use an objective and respectful language for the image descriptions.
+
+### Licensing Information
+
+CC BY 4.0
+
+### Citation Information
+
+```
+@misc{garg2024imageinwords,
+      title={ImageInWords: Unlocking Hyper-Detailed Image Descriptions}, 
+      author={Roopal Garg and Andrea Burns and Burcu Karagol Ayan and Yonatan Bitton and Ceslee Montgomery and Yasumasa Onoe and Andrew Bunner and Ranjay Krishna and Jason Baldridge and Radu Soricut},
+      year={2024},
+      eprint={2405.02793},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```

imageinwords.py

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+import datasets
+import json
+import pandas as pd
+import string
+
+_DESCRIPTION = """
+ImageInWords (IIW), a carefully designed human-in-the-loop annotation framework for curating hyper-detailed image descriptions and a new dataset resulting from this process.
+We validate the framework through evaluations focused on the quality of the dataset and its utility for fine-tuning with considerations for readability, comprehensiveness, specificity, hallucinations, and human-likeness.
+"""
+
+_HOMEPAGE = "https://google.github.io/imageinwords/"
+
+_LICENSE = "CC BY 4.0"
+
+
+_DATASET_GITHUB_PREFIX = "https://github.com/google/imageinwords/raw/main/datasets"
+
+_DATASET_GITHUB_URLS = {
+    "IIW-400": f"{_DATASET_GITHUB_PREFIX}/IIW-400/data.jsonl",
+    "DCI_Test": f"{_DATASET_GITHUB_PREFIX}/DCI_Test/data.jsonl",
+    "DOCCI_Test": f"{_DATASET_GITHUB_PREFIX}/DOCCI_Test/data.jsonl",
+    "CM_3600": f"{_DATASET_GITHUB_PREFIX}/CM_3600/data.jsonl",
+    "LocNar_Eval": f"{_DATASET_GITHUB_PREFIX}/LocNar_Eval/data.jsonl",
+}
+
+_DATASET_FEATURES = {
+    "IIW-400": datasets.Features({
+        "image/key": datasets.Value('string'),
+        "image/url": datasets.Value('string'),
+        "IIW": datasets.Value('string'),
+        "IIW-P5B": datasets.Value('string'),
+        "iiw-human-sxs-gpt4v": {
+            "metrics/Comprehensiveness": datasets.Value('string'),
+            "metrics/Specificity": datasets.Value('string'),
+            "metrics/Hallucination": datasets.Value('string'),
+            "metrics/First few line(s) as tldr": datasets.Value('string'),
+            "metrics/Human Like": datasets.Value('string'),
+        },
+        "iiw-human-sxs-iiw-p5b": {
+            "metrics/Comprehensiveness": datasets.Value('string'),
+            "metrics/Specificity": datasets.Value('string'),
+            "metrics/Hallucination": datasets.Value('string'),
+            "metrics/First few line(s) as tldr": datasets.Value('string'),
+            "metrics/Human Like": datasets.Value('string'),
+        },
+    }),
+    "DCI_Test": datasets.Features({
+        "image": datasets.Value('string'),
+        "ex_id": datasets.Value('string'),
+        "IIW": datasets.Value('string'),
+        "metrics/Comprehensiveness": datasets.Value('string'),
+        "metrics/Specificity": datasets.Value('string'),
+        "metrics/Hallucination": datasets.Value('string'),
+        "metrics/First few line(s) as tldr": datasets.Value('string'),
+        "metrics/Human Like": datasets.Value('string'),
+    }),
+    "DOCCI_Test": datasets.Features({
+        "image": datasets.Value('string'),
+        "image/thumbnail_url": datasets.Value('string'),
+        "IIW": datasets.Value('string'),
+        "DOCCI": datasets.Value('string'),
+        "metrics/Comprehensiveness": datasets.Value('string'),
+        "metrics/Specificity": datasets.Value('string'),
+        "metrics/Hallucination": datasets.Value('string'),
+        "metrics/First few line(s) as tldr": datasets.Value('string'),
+        "metrics/Human Like": datasets.Value('string'),
+    }),
+    "CM_3600": datasets.Features({
+        "image/key": datasets.Value('string'),
+        "image/url": datasets.Value('string'),
+        "IIW-P5B": datasets.Value('string'),
+    }),
+    "LocNar_Eval": datasets.Features({
+        "image/key": datasets.Value('string'),
+        "image/url": datasets.Value('string'),
+        "IIW-P5B": datasets.Value('string'),
+    }),
+}
+
+
+_CM_3600_URL_PATTERN = string.Template("https://open-images-dataset.s3.amazonaws.com/crossmodal-3600/$IMAGE_KEY.jpg")
+_DOCCI_AAR_URL_PATTERN = string.Template("https://storage.googleapis.com/docci/data/images_aar/$IMAGE_KEY.jpg")
+_DOCCI_THUMBNAIL_URL_PATTERN = string.Template("https://storage.googleapis.com/docci/thumbnails/$IMAGE_KEY.jpg")
+_LOCNAR_VALIDATION_URL_PATTERN = string.Template("https://open-images-dataset.s3.amazonaws.com/validation/$IMAGE_KEY.jpg")
+
+
+class ImageInWords(datasets.GeneratorBasedBuilder):
+    """ImageInWords dataset"""
+    
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="IIW-400", version=VERSION, description="IIW-400"),
+        datasets.BuilderConfig(name="DCI_Test", version=VERSION, description="DCI_Test"),
+        datasets.BuilderConfig(name="DOCCI_Test", version=VERSION, description="DOCCI_Test"),
+        datasets.BuilderConfig(name="CM_3600", version=VERSION, description="CM_3600"),
+        datasets.BuilderConfig(name="LocNar_Eval", version=VERSION, description="LocNar_Eval"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "IIW-400"
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            features=_DATASET_FEATURES[self.config.name],
+            homepage=_HOMEPAGE,
+            description=_DESCRIPTION,
+            license=_LICENSE,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        hf_auth_token = dl_manager.download_config.use_auth_token
+        if hf_auth_token is None:
+            raise ConnectionError(
+                "Please set use_auth_token=True or use_auth_token='<TOKEN>' to download this dataset"
+            )
+    
+        downloaded_file = dl_manager.download_and_extract(_DATASET_GITHUB_URLS[self.config.name])
+        if self.config.name == "LocNar_Eval":
+            split_type = datasets.Split.VALIDATION
+        else:
+            split_type = datasets.Split.TEST
+        return [
+            datasets.SplitGenerator(name=split_type, gen_kwargs={"filepath": downloaded_file}),
+        ]
+
+    def _generate_examples(self, filepath):
+        match self.config.name:
+            case "IIW-400":
+                return self._generate_examples_iiw_400(filepath)
+            case "DCI_Test":
+                return self._generate_examples_dci_test(filepath)
+            case "DOCCI_Test":
+                return self._generate_examples_docci_test(filepath)
+            case "CM_3600":
+                return self._generate_examples_cm_3600(filepath)
+            case "LocNar_Eval":
+                return self._generate_examples_locnar_eval(filepath)
+
+    def _generate_examples_iiw_400(self, filepath):
+        with open(filepath) as fp:
+            for json_line in fp:
+                json_obj = json.loads(json_line.strip())
+                json_obj["image/url"] = _DOCCI_AAR_URL_PATTERN.substitute(IMAGE_KEY=json_obj["image/key"])
+                yield json_obj["image/key"], json_obj
+
+    def _generate_examples_dci_test(self, filepath):
+        with open(filepath) as fp:
+            for json_line in fp:
+                json_obj = json.loads(json_line.strip())
+                yield json_obj["image"], json_obj
+
+    def _generate_examples_docci_test(self, filepath):
+        with open(filepath) as fp:
+            for json_line in fp:
+                json_obj = json.loads(json_line.strip())
+                json_obj["image/thumbnail_url"] = _DOCCI_THUMBNAIL_URL_PATTERN.substitute(IMAGE_KEY=json_obj["image"])
+                yield json_obj["image"], json_obj
+
+    def _generate_examples_cm_3600(self, filepath):
+        with open(filepath) as fp:
+            for json_line in fp:
+                json_obj = json.loads(json_line.strip())
+                json_obj["image/url"] = _CM_3600_URL_PATTERN.substitute(IMAGE_KEY=json_obj["image/key"])
+                del json_obj["image/source"]
+                yield json_obj["image/key"], json_obj
+                
+            
+    def _generate_examples_locnar_eval(self, filepath):
+        with open(filepath) as fp:
+            for json_line in fp:
+                json_obj = json.loads(json_line.strip())
+                json_obj["image/url"] = _LOCNAR_VALIDATION_URL_PATTERN.substitute(IMAGE_KEY=json_obj["image/key"])
+                del json_obj["image/source"]
+                yield json_obj["image/key"], json_obj