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Image classification, object detection, segmentation, face recognition, OCR, image generation, video understanding
17,713 datasets
Images of 20 different snack foods, sourced from the Google Open Images dataset release 2017_11. It includes CSV files with bounding box annotations for object detection, though not all images are annotated and some have multiple annotations. The dataset was created by Matthijs to accompany a machine learning book.
Serving as associated with research on faster and stabilized GAN training for high-fidelity few-shot image synthesis. It contains images, as indicated by the 'Modalityimage' tag, and is categorized as a small dataset with a size of 'n1 K'. The dataset was created by the author 'huggan' and last updated in April 2022.
A collection of images of dogs intended for few-shot image synthesis research, as referenced in the 2021 arXiv paper 'Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis'. It is hosted by the 'huggan' organization and was last updated in April 2022. The specific row count, column structure, and file size are not provided in the input.
Anime-style face images intended for few-shot image synthesis research. It was created by the 'huggan' organization and last updated on April 12, 2022. The dataset is associated with a 2021 research paper on faster and stabilized GAN training for high-fidelity few-shot image synthesis.
Skull images used for research on few-shot image synthesis with Generative Adversarial Networks (GANs). It is associated with the 2021 arXiv paper 'Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis' by Bingchen Liu et al. The dataset is hosted on Hugging Face and was last updated in April 2022.
Encompassing unpaired images of Yosemite in summer and winter seasons, originally part of the CycleGAN benchmark datasets. It is used for training unpaired image-to-image translation models, as described in the associated research paper.
Images of Grumpy Cat, curated for few-shot image synthesis research. It is associated with a 2021 arXiv paper on faster and stabilized GAN training for high-fidelity few-shot image synthesis. The dataset is categorized as having a size of 'n1 K', indicating it contains approximately 1,000 images.
Images for few-shot image synthesis, associated with a 2021 research paper on faster and stabilized GAN training. It is categorized as containing 1K to 10K samples (Size Categoriesn1 K) and includes image modality data. The dataset was last updated in April 2022.
Serving as associated with research on faster and stabilized GAN training for high-fidelity few-shot image synthesis. It contains images of Barack Obama, as indicated by the title and tags, and is hosted on Hugging Face. The dataset size is categorized as 'n1 K', suggesting it contains on the order of 1,000 items.
Images in the Fauvism art style, curated for few-shot image synthesis research. It is associated with a 2021 academic paper on faster and stabilized GAN training for high-fidelity few-shot image synthesis. The dataset size is categorized as 'n1 K', indicating it contains approximately 1,000 items.
Images of cats intended for few-shot image synthesis research. It is associated with a 2021 arXiv paper on faster and stabilized GAN training for high-fidelity few-shot image synthesis. The dataset size and specific column structure are not provided.
Featuring images of flat colored patterns, curated for research on few-shot image synthesis and Generative Adversarial Network (GAN) training. It is associated with a 2021 arXiv paper by Bingchen Liu et al. on faster and stabilized GAN training for high-fidelity few-shot image synthesis.
Comprising unpaired images of flowers captured by iPhone and DSLR cameras, originally part of the CycleGAN benchmark datasets. It is used for unpaired image-to-image translation tasks, as described in the associated research paper from 2017. The dataset is hosted on Hugging Face by the 'huggan' organization.
A collection of unpaired images from the Cityscapes collection, originally part of the CycleGAN project for image-to-image translation. It is associated with the research paper 'Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks' (arXiv:1703.10593). The dataset was last updated on the Hugging Face platform in April 2022.
Serving as part of the CycleGAN datasets collection, originally hosted by researchers at UC Berkeley. It is used for unpaired image-to-image translation tasks as described in the associated 2017 arXiv paper. The dataset contains images, but the specific row count, column structure, and file formats are unknown.
Featuring images for few-shot image synthesis research, associated with a 2021 paper on faster and stabilized GAN training. The dataset is categorized as 'Size Categoriesn1 K', indicating a scale of approximately 1,000 items. It was last updated in April 2022.
A text dataset based on the CNN/DailyMail news summarization corpus, enhanced with coreference annotations. The dataset was created by user 'cestwc' and uploaded to Hugging Face in May 2022. It is designed for training and evaluating models on the task of resolving pronoun references within news narratives.
Featuring facade images used for unpaired image-to-image translation, originating from the CycleGAN project. It is part of the datasets associated with the 2017 research paper on Cycle-Consistent Adversarial Networks (arXiv:1703.10593). The dataset was uploaded by 'huggan' and last updated in April 2022.
Serving as part of the CycleGAN datasets for unpaired image-to-image translation. It contains collections of images from two distinct domains, ukiyoe and photographic, as referenced in the associated arXiv paper. The specific row count, column count, and dataset size are not provided in the input.
Approximately 1.5 million images from the Conceptual Captions 12M (CC12M) collection, encoded into feature representations using a VQGAN f16 1024 model. The dataset was created by johnowhitaker and last updated in April 2022. A script for further preprocessing is provided in the repository.