Multi-modal
SWE-bench_Multimodal
SWE-bench Multimodal
Dataset Summary
SWE-bench Multimodal is a dataset that tests systems' ability to resolve real-world GitHub issues in visual software domains. Unlike the original SWE-bench, which is Python-only and text-only, every task instance here comes from a JavaScript or TypeScript repository and carries at least one image asset — a screenshot, a screen recording, a diagram, or a rendering of incorrect output.
The dataset collects 612 Issue-Pull Request pairs from 17… See the full description on the dataset page: https://huggingface.co/datasets/SWE-bench/SWE-bench_Multimodal.Multimodal-Mind2Web
Dataset Summary
Multimodal-Mind2Web is the multimodal version of Mind2Web, a dataset for developing and evaluating generalist agents
for the web that can follow language instructions to complete complex tasks on any website. In this dataset, we align each HTML document in the dataset with
its corresponding webpage screenshot image from the Mind2Web raw dump. This multimodal version addresses the inconvenience of loading images from the ~300GB Mind2Web Raw Dump.
Dataset… See the full description on the dataset page: https://huggingface.co/datasets/osunlp/Multimodal-Mind2Web.multimodal_data_annotator_datasetMaterials dataset consisting of spatial and time resolved versions of the same object. Specially curated for the annotator such that for each object, time resolved signal may be viewed alongside the RGB and for different graphs/forms
SWE-bench_Multimodal
SWE-bench Multimodal
SWE-bench Multimodal is a dataset of 617 task instances that evalutes Language Models and AI Systems on their ability to resolve real world GitHub issues.
To learn more about the dataset, please visit our website.
More updates coming soon!
Zebra-CoT
Zebra‑CoT
A diverse large-scale dataset for interleaved vision‑language reasoning traces.
Dataset Description
Zebra‑CoT is a diverse large‑scale dataset with 182,384 samples containing logically coherent interleaved text‑image reasoning traces across four major categories: scientific reasoning, 2D visual reasoning, 3D visual reasoning, and visual logic & strategic games.
Dataset Structure
Each example in Zebra‑CoT consists of:
Problem statement:… See the full description on the dataset page: https://huggingface.co/datasets/multimodal-reasoning-lab/Zebra-CoT.multimodal-ct-radiology-reports
Perle AI Multi-phase CECT and CT with Radiology Reports
Summary
A de-identified CT dataset from Perle AI, paired with the original radiology reports. It supports work on multi-modal medical imaging: phase or pathology classification, report generation from images, and visual question answering.
The release has three configurations:
Config
Modality
Subjects
Pairing
cect_3phase
3-phase contrast-enhanced abdominal CT (DICOM)
5
per-subject text report +… See the full description on the dataset page: https://huggingface.co/datasets/Perle-ai/multimodal-ct-radiology-reports.