Panda
Datasets
All datasets matching “Panda”pandabench
PandaBench
Paper | Project Page | Code
PandaBench (and PandaSet) is an image distortion benchmark designed for evaluating perceptual comparison and distortion-aware visual reasoning. It introduces the task of learning a Distortion Graph (DG), representing dense degradation information such as distortion type, severity, and quality scores in a compact, interpretable graph structure grounded in image regions.
PandaSet (the train/val folders) is used to train the model (Panda), while… See the full description on the dataset page: https://huggingface.co/datasets/kjanjua26/pandabench.panda-bench
PandaBench
PandaBench is a comprehensive benchmark for evaluating Large Language Model (LLM) safety, focusing on jailbreak attacks, defense mechanisms, and evaluation methodologies.
The PandaGuard framework architecture illustrating the end-to-end pipeline for LLM safety evaluation. The system connects three key components: Attackers, Defenders, and Judges.
Dataset Description
This repository contains the benchmark results from extensive evaluations of various… See the full description on the dataset page: https://huggingface.co/datasets/Beijing-AISI/panda-bench.pandasetPandaSet aims to promote and advance research and development in autonomous driving and machine learning.
The first open-source dataset made available for both academic and commercial use, PandaSet combines Hesai’s best-in-class LiDAR sensors with Scale AI’s high-quality data annotation.
PandaSet features data collected using a forward-facing LiDAR with image-like resolution (PandarGT) as well as a mechanical spinning LiDAR (Pandar64).
The collected data was annotated with a combination of… See the full description on the dataset page: https://huggingface.co/datasets/georghess/pandaset.panda-pick-place-10-43-37_22-05-2026Panda-CVL-train
Panda-CVL Training Split
Overview
Panda-CVL is a token-level correction dataset and benchmark annotated with the onPanda tool.
Given a question-response pair, the model first judges whether the response is acceptable.
If correction is needed, it must locate the first inappropriate token and replace it with an
appropriate one, so generation can continue from the "correct prefix + corrected token" state
and ultimately produce an acceptable response.
Compared with… See the full description on the dataset page: https://huggingface.co/datasets/diyer22/Panda-CVL-train.panda-pick-place-03-02-43_26-05-2026