sra
Datasets
All datasets matching “sra”MapPool
MapPool - Bubbling up an extremely large corpus of maps for AI
MapPool is a dataset of 75 million potential maps and textual captions. It has been derived from CommonPool, a dataset consisting of 12 billion text-image pairs from the Internet. The images have been encoded by a vision transformer and classified into maps and non-maps by a support vector machine. This approach outperforms previous models and yields a validation accuracy of 98.5%. The MapPool dataset may help to train… See the full description on the dataset page: https://huggingface.co/datasets/sraimund/MapPool.srankmonsternobehemothdakedonekotomachigawareteelfmusumenopettoshitekurashitemasu
Bangumi Image Base of S-rank Monster No "behemoth" Dakedo, Neko To Machigawarete Elf Musume No Pet Toshite Kurashitemasu
This is the image base of bangumi S-Rank Monster no "Behemoth" dakedo, Neko to Machigawarete Elf Musume no Pet toshite Kurashitemasu, we detected 56 characters, 4649 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this… See the full description on the dataset page: https://huggingface.co/datasets/BangumiBase/srankmonsternobehemothdakedonekotomachigawareteelfmusumenopettoshitekurashitemasu.sr-artifact-prominence
SR Artifact Prominence
Annotated super-resolution artifact regions across four image subsets, with
crowdsourced per-region prominence scores, artifact type labels, and
natural-language descriptions.
Prominence is the fraction of valid crowd workers who answered that the
highlighted region contains a noticeable super-resolution artifact.
Subsets
Subset
Source dataset
Source images
Masks
Notes
open_images
Open Images
547
1,523
GT + LR-bicubic + multiple SR… See the full description on the dataset page: https://huggingface.co/datasets/imolodetskikh/sr-artifact-prominence.CountQA
Dataset Summary
CountQA is the new benchmark designed to stress-test the Achilles' heel of even the most advanced Multimodal Large Language Models (MLLMs): object counting. While modern AI demonstrates stunning visual fluency, it often fails at this fundamental cognitive skill, a critical blind spot limiting its real-world reliability.
This dataset directly confronts that weakness with over 1,500 challenging question-answer pairs built on real-world images, hand-captured to feature… See the full description on the dataset page: https://huggingface.co/datasets/Jayant-Sravan/CountQA.imagenet_resized_64x64This is an upload of imagenet_resized/64x64 from tensorflow datasets, (but shuffled before uploading).
The homepage of imagenet_resized is: https://patrykchrabaszcz.github.io/Imagenet32/
imagenet_resized is a derivative of imagenet (and also available to download from there): https://image-net.org/index.php
Warning: The integer labels used are defined by the authors and do not match those from the other ImageNet datasets provided by Tensorflow datasets. See the original label list, and the… See the full description on the dataset page: https://huggingface.co/datasets/sradc/imagenet_resized_64x64.eagle-iter267-sra-phase1-enc