Omkar2118/datasets huggingface dataset:
RACECAR -- The Dataset for High-Speed Autonomous Racing

This paper describes the first open dataset for full-scale and high-speed autonomous racing. Multi-modal sensor data has been collected from fully autonomous Indy race cars operating at speeds of up to 170 mph (273 kph). Six teams who raced in the Indy Autonomous Challenge have contributed to this dataset. The dataset spans 11 interesting racing scenarios across two race tracks which include solo laps, multi-agent laps, overtaking situations, high-accelerations, banked tracks, obstacle avoidance, pit entry and exit at different speeds. The dataset contains data from 27 racing sessions across the 11 scenarios with over 6.5 hours of sensor data recorded from the track. The data is organized and released in both ROS2 and nuScenes format. We have also developed the ROS2-to-nuScenes conversion library to achieve this. The RACECAR data is unique because of the high-speed environment of autonomous racing. We present several benchmark problems on localization, object detection and tracking (LiDAR, Radar, and Camera), and mapping using the RACECAR data to explore issues that arise at the limits of operation of the vehicle.
pdsdpo-v1_0-data

PDS-DPO-9K

Introducing paper: Multimodal Preference Data Synthetic Alignment with Reward Model
DRAM Dataset
Full name: Diverse Realism in Art Movements

DRAM dataset is the first dataset to introduce a fully labeled test set for the task of semantic segmentation of art paintings. The dataset uses a subset of 12 classes used in the PascalVoc12 dataset:  Bird, Boat, Bottle, Cat, Chair, Cow, Dog,Horse, Sheep, Person, Potted-Plant, and Background.

The dataset consists of 5677 unlabeled and 718 labeled paintings from 152 painters. The dataset is divided into 5 categories: Realism, Impressionism, Post-Impressionism, Expressionism and 'Unseen', each holding paintings from a specific art movement. the Unseen category appears only in the test set and it consists from 135 image of the following art movements: Art-Nouveau, Baroque, Cubism, Divisionism, Fauvism, Chinese Ink and Wash, Japonism and Rococo.

The dataset was constructed for the perceptual task of understanding how computers see images from various styles.

Potential use cases:
1) Evaluating semantic segmentation models on a diverse and complex domain.
2) Investigation on latent representations of abstraction-varying art styles
3) Domain adaptation solutions for semantic segmentation of art paintings.

Introducing paper: Semantic Segmentation in Art Paintings