datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
ArtiMuse-10K
ArtiMuse:
Fine-Grained Image Aesthetics Assessment with Joint Scoring and Expert-Level Understanding
[🌐 Project Page]
[🚀 Online Demo]
[💻 Code]
[📄 Paper]
[[🧩 Checkpoints: 🤗 Hugging Face | 🤖 ModelScope]]
🌟 Building upon on ArtiMuse, we introduce UniPercept, a comprehensive follow-up work that provides a meticulous study on perceptual-level image understanding. It spans Image Aesthetics Assessment (IAA), Image Quality Assessment (IQA), and Image Structure & Texture… See the full description on the dataset page: https://huggingface.co/datasets/Thunderbolt215215/ArtiMuse-10K.thunderboltfantasy
Bangumi Image Base of Thunderbolt Fantasy
This is the image base of bangumi Thunderbolt Fantasy, we detected 21 characters, 1926 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 dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the… See the full description on the dataset page: https://huggingface.co/datasets/BangumiBase/thunderboltfantasy.UniPercept-Bench
UniPercept: Towards Unified Perceptual-Level Image Understanding across Aesthetics, Quality, Structure, and Texture
Shuo Cao*,
Jiayang Li*,
Xiaohui Li,
Yuandong Pu,
Kaiwen Zhu,
Yuanting Gao,
Siqi Luo,
Yi Xin,
Qi Qin,
Yu Zhou,
Xiangyu Chen,
Wenlong Zhang,
Bin Fu,
Yu Qiao,
Yihao Liu†
University of Science and Technology of China
Shanghai AI Laboratory
Peking University
* Equal contribution
† Corresponding author
If you find this… See the full description on the dataset page: https://huggingface.co/datasets/Thunderbolt215215/UniPercept-Bench.