datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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_26-05-2026panda-pick-place-07-44-43_22-05-2026robocasa-x-sp900-panda-flip-mug-upright
RoboCasa-X SP-900 Panda Flip Mug Upright
Processed RLDS/TFDS data released with
BARX: Cross-Embodiment Transfer via Behavior-Aligned Representations.
Dataset summary
Paper dataset: SP-900
Task: Flip Mug Upright
Embodiment(s): Panda
Episodes: 900
Origin: MimicGen-generated simulator demonstrations
TFDS builder id: robocasa_x_sp900_panda_flip_mug_upright
Language: English task instructions
Each step exposes a 7-D policy action
[dx, dy, dz, droll, dpitch, dyaw… See the full description on the dataset page: https://huggingface.co/datasets/ajaysri/robocasa-x-sp900-panda-flip-mug-upright.SeeU45_PreProcessedpanda_v2robocasa-x-sp900-panda-og-flip-mug-upright
RoboCasa-X SP-900 Panda-OG Flip Mug Upright
Processed RLDS/TFDS data released with
BARX: Cross-Embodiment Transfer via Behavior-Aligned Representations.
Dataset summary
Paper dataset: SP-900
Task: Flip Mug Upright
Embodiment(s): Panda-OG
Episodes: 900
Origin: MimicGen-generated simulator demonstrations
TFDS builder id: robocasa_x_sp900_panda_og_flip_mug_upright
Language: English task instructions
Each step exposes a 7-D policy action
[dx, dy, dz, droll, dpitch… See the full description on the dataset page: https://huggingface.co/datasets/ajaysri/robocasa-x-sp900-panda-og-flip-mug-upright.BRIDGE
BRIDGE — Qwen Image Edit Dataset
Part of the dataset used in BRIDGE: Background Routing and Isolated Discrete Gating for Coarse-Mask Local Editing.
FLUX subject-condition extension (2026-09-19)
The same BRIDGE method is trained with LoRA on Qwen and full-transformer
fine-tuning on FLUX. The FLUX dataset adds a generated subject-reference image
condition. See the extension documentation.
Exact FLUX split: 27,834 training rows, 3,092 test rows.
30,926 selected… See the full description on the dataset page: https://huggingface.co/datasets/PANDATREE/BRIDGE.Panda-CVL-test
Panda-CVL Test Split
This dataset is based on the paper onPanda: Efficient Annotation of On-Policy Alignment Data for LLMs and Agents via Token-Level Correction. Code is available at GitHub.
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… See the full description on the dataset page: https://huggingface.co/datasets/diyer22/Panda-CVL-test.robocasa-x-sp900-panda-turn-on-sink-faucet
RoboCasa-X SP-900 Panda Turn On Sink Faucet
Processed RLDS/TFDS data released with
BARX: Cross-Embodiment Transfer via Behavior-Aligned Representations.
Dataset summary
Paper dataset: SP-900
Task: Turn On Sink Faucet
Embodiment(s): Panda
Episodes: 900
Origin: MimicGen-generated simulator demonstrations
TFDS builder id: robocasa_x_sp900_panda_turn_on_sink_faucet
Language: English task instructions
Each step exposes a 7-D policy action
[dx, dy, dz, droll, dpitch… See the full description on the dataset page: https://huggingface.co/datasets/ajaysri/robocasa-x-sp900-panda-turn-on-sink-faucet.robocasa-x-sp900-panda-pnp
RoboCasa-X SP-900 Panda PnP Counter to Sink / PnP Sink to Counter
Processed RLDS/TFDS data released with
BARX: Cross-Embodiment Transfer via Behavior-Aligned Representations.
Dataset summary
Paper dataset: SP-900
Task: PnP Counter to Sink / PnP Sink to Counter
Embodiment(s): Panda
Episodes: 1800
Origin: MimicGen-generated simulator demonstrations
TFDS builder id: robocasa_x_sp900_panda_pnp
Language: English task instructions
Each step exposes a 7-D policy action… See the full description on the dataset page: https://huggingface.co/datasets/ajaysri/robocasa-x-sp900-panda-pnp.robocasa-x-sp900-panda-og-pnp
RoboCasa-X SP-900 Panda-OG PnP Counter to Sink / PnP Sink to Counter
Processed RLDS/TFDS data released with
BARX: Cross-Embodiment Transfer via Behavior-Aligned Representations.
Dataset summary
Paper dataset: SP-900
Task: PnP Counter to Sink / PnP Sink to Counter
Embodiment(s): Panda-OG
Episodes: 1800
Origin: MimicGen-generated simulator demonstrations
TFDS builder id: robocasa_x_sp900_panda_og_pnp
Language: English task instructions
Each step exposes a 7-D… See the full description on the dataset page: https://huggingface.co/datasets/ajaysri/robocasa-x-sp900-panda-og-pnp.Machine_Mindset_MBTI_datasetHere are the behavior datasets used for supervised fine-tuning (SFT). And they can also be used for direct preference optimization (DPO).
The exact copy can also be found in Github.
Prefix 'en' denotes the datasets of the English version.
Prefix 'zh' denotes the datasets of the Chinese version.
Dataset introduction
There are four dimension in MBTI. And there are two opposite attributes within each dimension.
To be specific:
Energe: Extraversion (E) - Introversion (I)… See the full description on the dataset page: https://huggingface.co/datasets/pandalla/Machine_Mindset_MBTI_dataset.robocasa-x-sp900-panda-og-turn-on-sink-faucet
RoboCasa-X SP-900 Panda-OG Turn On Sink Faucet
Processed RLDS/TFDS data released with
BARX: Cross-Embodiment Transfer via Behavior-Aligned Representations.
Dataset summary
Paper dataset: SP-900
Task: Turn On Sink Faucet
Embodiment(s): Panda-OG
Episodes: 900
Origin: MimicGen-generated simulator demonstrations
TFDS builder id: robocasa_x_sp900_panda_og_turn_on_sink_faucet
Language: English task instructions
Each step exposes a 7-D policy action
[dx, dy, dz, droll… See the full description on the dataset page: https://huggingface.co/datasets/ajaysri/robocasa-x-sp900-panda-og-turn-on-sink-faucet.single_panda_gripper-OpenSingleDoorsingle_panda_gripper-CloseDoubleDoorpanda-10mGrab_panda_2This dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"robot_type": "so100",
"total_episodes": 2,
"total_frames": 1161,
"total_tasks": 1,
"total_videos": 4,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:2"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/Xiewei1211/Grab_panda_2.single_panda_gripper_PnPCabToCounter_poisoned_trainstarcoder-numpy-pandasEngineMT-QA
EngineMT-QA Dataset
Overview
EngineMT-QA is a large-scale, multi-task, multimodal dataset for Time-Series Question Answering (Time-Series QA). It enables research on aligning multivariate time-series signals with natural language through four key cognitive tasks:
Understanding
Perception
Reasoning
Decision-Making
The dataset is built on N-CMAPSS, simulating real-world aero-engine operational and maintenance scenarios. It supports the development and evaluation of… See the full description on the dataset page: https://huggingface.co/datasets/pandalin98/EngineMT-QA.Franka_panda_parallel_hand_realThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"robot_type": "franka",
"total_episodes": 0,
"total_frames": 0,
"total_tasks": 0,
"total_videos": 0,
"total_chunks": 0,
"chunks_size": 1000,
"fps": 15,
"splits": {},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/GVLA/Franka_panda_parallel_hand_real.single_panda_gripper-PnPCabToCountereeg-ai-2026single_panda_gripper-CoffeeSetupMugsingle_panda_gripper-PnPCounterToMicrowave