A dataset of 30,000 images with 5 captions per image. The dataset was created by researchers at Stanford University and is used for research in machine learning and natural language processing tasks such as image captioning and visual question answering.

The Flickr30K Entities dataset is an extension to the Flickr30K dataset. It augments the original 158k captions with 244k coreference chains, linking mentions of the same entities across different captions for the same image, and associating them with 276k manually annotated bounding boxes. This is used to define a new benchmark for localization of textual entity mentions in an image.

Large-scale Multi-modality Models Evaluation Suite

Accelerating the development of large-scale multi-modality models (LMMs) with lmms-eval

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  This Dataset

This is a formatted version of flickr30k. It is used in our lmms-eval pipeline to allow for one-click evaluations of large multi-modality models. @article{young-etal-2014-image, title = "From image descriptions to visual denotations: New similarity… See the full description on the dataset page: https://huggingface.co/datasets/lmms-lab/flickr30k.

Dataset Card for Flickr30k Captions

This dataset is a collection of caption pairs given to the same image, collected from Flickr30k. See Flickr30k for additional information. This dataset can be used directly with Sentence Transformers to train embedding models. Note that two captions for the same image do not strictly have the same semantic meaning.

  Dataset Subsets







  pair subset

Columns: "caption1", "caption2" Column types: str, str Examples:{… See the full description on the dataset page: https://huggingface.co/datasets/sentence-transformers/flickr30k-captions.

Dataset Card for "flickr30k-captions"

  Dataset Summary

We propose to use the visual denotations of linguistic expressions (i.e. the set of images they describe) to define novel denotational similarity metrics, which we show to be at least as beneficial as distributional similarities for two tasks that require semantic inference. To compute these denotational similarities, we construct a denotation graph, i.e. a subsumption hierarchy over constituents and their denotations… See the full description on the dataset page: https://huggingface.co/datasets/embedding-data/flickr30k_captions_quintets.

Former Flickr30k-CN translates the training and validation sets of Flickr30k using machine translation and manually translates the test set. We check the machine-translated results and find two kinds of problems. (1) Some sentences have language problems and translation errors. (2) Some sentences have poor semantics. In addition, the different translation ways between the training set and test set prevent the model from achieving accurate performance. We gather 6 professional English and Chinese linguists to meticulously re-translate all data of Flickr30k and double-check each sentence.

This dataset, based on Flickr30K, is introduced in Learning with Noisy Correspondence for Cross-modal Matching. Noisy correspondence is simulated by randomly shuffling the captions of training images for a specific percentage, denoted by noise ratio

Explicit Caption Editing (ECE) — refining reference image captions through a sequence of explicit edit operations (e.g., KEEP, DELETE) — has raised significant attention due to its explainable and human-like nature.

jimmeylove/Flickr30k dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset

This dataset was created by Safia Faiz

Contents

Original Flickr-30K dataset in TFRecord format for faster computations on a TPU.

1. Original dataset contained 5 captions against each image.
2. Each .tfrec file contains all 30k images but has only one caption for each image, the original caption number is denoted by set in the filename.
3. Therefore, Caption #1 for image XXXXXX.jpg will be in complete_30K_set1.tfrec and so on.
4. Each TFRecord contains the following features ``` feature = { 'image': _bytes_feature(feature), #jpeg image 'name': _bytes_feature(name), #filename 'comment': _bytes_feature(comment), #caption

} ``4. Each caption is encapsulated with

Source

Uploaded from https://github.com/danieljf24/w2vv

The Flickr 30k dataset is a large-scale image captioning dataset containing 30,000 images with 30 captions each.

recastai/flickr30k-augmented-caption dataset hosted on Hugging Face and contributed by the HF Datasets community

Dev24910/flickr30k dataset hosted on Hugging Face and contributed by the HF Datasets community

Advancements in deep learning have revolutionized numerous real-world applications, including image recognition, visual question answering, and image captioning. Among these, image captioning has emerged as a critical area of research, with substantial progress achieved in Arabic, Chinese, Uyghur, Hindi, and predominantly English. However, despite Urdu being a morphologically rich and widely spoken language, research in Urdu image captioning remains underexplored due to a lack of resources. This study creates a new Urdu Image Captioning Dataset (UCID) called UC-23-RY to fill in the gaps in Urdu image captioning. The Flickr30k dataset inspired the 159,816 Urdu captions in the dataset. Additionally, it suggests deep learning architectures designed especially for Urdu image captioning, including NASNetLarge-LSTM and ResNet-50-LSTM. The NASNetLarge-LSTM and ResNet-50-LSTM models achieved notable BLEU-1 scores of 0.86 and 0.84 respectively, as demonstrated through evaluation in this study accessing the model’s impact on caption quality. Additionally, it provides useful datasets and shows how well-suited sophisticated deep learning models are for improving automatic Urdu image captioning.

The Flickr30k dataset has become a standard benchmark for sentence-based image description. This paper presents Flickr30k Entities, which augments the 158k captions from Flickr30k with 244k coreference chains, linking mentions of the same entities across different captions for the same image, and associating them with 276k manually annotated bounding boxes. Such annotations are essential for continued progress in automatic image description and grounded language understanding. They enable us to define a new benchmark for localization of textual entity mentions in an image. We present a strong baseline for this task that combines an image-text embedding, detectors for common objects, a color classifier, and a bias towards selecting larger objects.

The splits were created by Andrej Karpathy and is predominently useful for Image Captioning purpose. Contains captions for Flickr8k, Flickr30k and MSCOCO datasets. And the datasets has been divided into train, test and validation splits.

Source: http://cs.stanford.edu/people/karpathy/deepimagesent/caption_datasets.zip

Advancements in deep learning have revolutionized numerous real-world applications, including image recognition, visual question answering, and image captioning. Among these, image captioning has emerged as a critical area of research, with substantial progress achieved in Arabic, Chinese, Uyghur, Hindi, and predominantly English. However, despite Urdu being a morphologically rich and widely spoken language, research in Urdu image captioning remains underexplored due to a lack of resources. This study creates a new Urdu Image Captioning Dataset (UCID) called UC-23-RY to fill in the gaps in Urdu image captioning. The Flickr30k dataset inspired the 159,816 Urdu captions in the dataset. Additionally, it suggests deep learning architectures designed especially for Urdu image captioning, including NASNetLarge-LSTM and ResNet-50-LSTM. The NASNetLarge-LSTM and ResNet-50-LSTM models achieved notable BLEU-1 scores of 0.86 and 0.84 respectively, as demonstrated through evaluation in this study accessing the model’s impact on caption quality. Additionally, it provides useful datasets and shows how well-suited sophisticated deep learning models are for improving automatic Urdu image captioning.

This dataset contains images of realistic style manga pages and photos of people. Each image has size of 460x660. The purpose of this dataset to find out the possibility of style transferring of manga to photo and vice versa. I've used cycleGAN for this purpose.