datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
MMArt-PPR10k
MMArt-PPR10k Dataset
The MMArt-PPR10k is a multimodal dataset specifically created for research into the instruction-driven agentic image retouching task. It is built upon the original PPR10k dataset and offers rich, paired image data, user instructions, and information on the Lua/XMP tools used in Lightroom.
Dataset Structure
The dataset is organized into a hierarchical folder structure. Each data sample corresponds to a specific image pair and its related files, located… See the full description on the dataset page: https://huggingface.co/datasets/JarvisArt/MMArt-PPR10k.open-schematics
Open Schematics Dataset
A comprehensive dataset of electronic schematics from hardware projects. This dataset is designed for training AI models on circuit design, component recognition, and hardware engineering tasks.
Dataset Description
This dataset contains electronic schematic files along with their visual representations, component information, and metadata from various hardware projects.
Dataset Structure
Each record in the dataset contains:
schematic:… See the full description on the dataset page: https://huggingface.co/datasets/jarvisemitra/open-schematics.RobustVLGuard
🛡️ RobustVLGuard
RobustVLGuard is a multimodal safety dataset designed to improve the robustness of Vision-Language Models (VLMs) against Gaussian noise and perturbation-based adversarial attacks. The dataset contains three carefully curated subsets: aligned safety data, misaligned safety data, and safety-agnostic general instruction-following data.
📄 Paper: Safeguarding Vision-Language Models: Mitigating Vulnerabilities to Gaussian Noise in Perturbation-based Attacks🔗 Code:… See the full description on the dataset page: https://huggingface.co/datasets/Jarvis1111/RobustVLGuard.MMArt-BenchJarvisArt: Liberating Human Artistic Creativity via an Intelligent Photo Retouching Agent.
🌍 Introduction
MMArt-Bench. To provide a comprehensive evaluation of JarvisArt’s performance, we introduce the MMArt-Bench, which is sampled from the MMArt dataset. It includes four main scenarios: portrait, landscape, street scenes, and still life, with 50 instances per category, totaling 200 instances. Each primary category contains multiple subcategories. For region-level evaluation, we… See the full description on the dataset page: https://huggingface.co/datasets/JarvisArt/MMArt-Bench.ArtEdit-BenchJarvisEvo: Towards a Self-Evolving Photo Editing Agent with Synergistic Editor-Evaluator Optimization.
🌍 Introduction
ArtEdit-Bench. We construct ArtEdit-Bench, comprising two subsets: (1) ArtEdit-Bench-Lr (800 samples: 400 English, 400 Chinese), selected from the ArtEdit-Lr dataset. It evaluates both global and local fine-grained retouching capabilities. (2) ArtEdit-Bench-Eval (200 English samples), sampled from ArtEdit-Eval, is used to fairly assess the model’s self-evaluation… See the full description on the dataset page: https://huggingface.co/datasets/JarvisEvo/ArtEdit-Bench.microscopygpt_jarvisdft_2dmicroscopygpt_jarvisdft_2dJARVIS-trainfashion-controlnet-canny