Omkar2118/datasets huggingface dataset:
AdvBench

To systematically evaluate the effectiveness of our approach at accomplishing this, we designed a new benchmark, AdvBench, based on two distinct settings. 

1. Harmful Strings: A collection of 500 strings that reflect harmful or toxic behavior, encompassing a wide spectrum of detrimental content such as profanity, graphic depictions, threatening behavior, misinformation, discrimination, cybercrime, and dangerous or illegal suggestions. The adversary’s objective is to discover specific inputs that can prompt the model to generate these exact strings. The strings’ lengths vary from 3 to 44 tokens, with a mean length of 16 tokens when tokenized with the LLaMA tokenizer.

2. Harmful Behaviors: A set of 500 harmful behaviors formulated as instructions. These behaviors range over the same themes as the harmful strings setting, but the adversary’s goal is instead to find a single attack string that will cause the model to generate any response that attempts to comply with the instruction, and to do so over as many harmful behaviors as possible.
Polarimetric Imaging for Perception

The dataset includes polarimetric, RGB and depth automotive (on the road) data.

The data includes synchronized and aligned samples of the following: angle of linear polarization (AoLP) images, degree of linear polarization (DoLP) images, RGB images, lidar scans, ground truth free space segmentation (road segmentation), GNSS / IMU readings (vehicle location, vehicle orientation, vehicle speed, vehicle acceleration, etc.) and calibration matrices.

The dataset includes 12,627 samples  from 6 different locations. The locations represent typical suburbs where the scenes are not remarkably cluttered, but provide a good distribution of vehicles, pedestrians and buildings.
Additionally, the dataset includes free space segmentation of 8,141 images.

Introducing paper: Polarimetric Imaging for Perception