datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Amazon-Reviews-2023Amazon Review 2023 is an updated version of the Amazon Review 2018 dataset.
This dataset mainly includes reviews (ratings, text) and item metadata (desc-
riptions, category information, price, brand, and images). Compared to the pre-
vious versions, the 2023 version features larger size, newer reviews (up to Sep
2023), richer and cleaner meta data, and finer-grained timestamps (from day to
milli-second).aopstwo-wheeler-yolo
Two-Wheeler YOLO Dataset
Merged from:
IDD Detection (~6,680 images) — Indian Driving Dataset, VOC XML → YOLO
COCO 2017 (~3,661 images) — motorcycle subset, COCO JSON → YOLO
Total: ~10,341 images | Class 0: two_wheeler | Format: YOLOv8
sparsh-skin-dataset
Sparsh-skin dataset
For Sparsh-skin the dataset consists of ~4 hours of contact data with different types of household objects, collected via a VR teleoperation using the Meta Quest 3.
There are 14 different objects in the dataset, each containing 10 sequences per object, which are each ~2 mins long. Every object has varied interaction with the object, including sliding, tapping, object reorientation in the hand, and the like.
We provide the sequences used for SSL training in an… See the full description on the dataset page: https://huggingface.co/datasets/facebook/sparsh-skin-dataset.CAViAR
CAViAR (val / test)
Causal Accident Video and Incident Analysis Repository — Nexar validation/test annotations.
This Hub dataset mirrors the public val/test split from github.com/nec-labs-ma/CAViAR: 749 videos, 7,407 QA pairs. Videos are not included.
video_path is the numeric Nexar clip id (e.g. 00284). Source videos: nexar-ai/nexar_collision_prediction ({id}.mp4 under train/ / test-public/ / test-private/).
Paper: CAViAR: A Causal Video Dataset for Fine-Grained Accident… See the full description on the dataset page: https://huggingface.co/datasets/sparshgarg57/CAViAR.sparsh-x-dataset
Dataset Details
This dataset contains the sequences used for training Sparsh-X, a multisensory touch encoder for the Digit 360 sensor. Sparsh-X allows to fuse multiple touch modalities into a single embedding, such as tactile image, audio from contact microphones, IMU and pressure data.
Our Sparsh-X training dataset is generated from two primary sources: an Allegro hand with Digit 360 sensors on the fingertips that performs random motions with objects such as dipping into a tray… See the full description on the dataset page: https://huggingface.co/datasets/facebook/sparsh-x-dataset.mapillary_traffic_signs
📄 Paper
If you use this dataset, please cite the following paper:
MAPILLARY VISTAS VALIDATION FOR FINE-GRAINED TRAFFIC SIGNS: A Benchmark Revealing Vision-Language Model LimitationsSubmitted to ICCV Workshop 2025
📄 Citation
@article{garg2025mapillary,
title={Mapillary Vistas Validation for Fine-Grained Traffic Signs: A Benchmark Revealing Vision-Language Model Limitations},
author={Garg, Sparsh and Aich, Abhishek},
journal={arXiv preprint arXiv:2508.02047}… See the full description on the dataset page: https://huggingface.co/datasets/sparshgarg57/mapillary_traffic_signs.aopsonlyprobsfiltered_lm_arenamaas-openenv-rl-datasetnew1millsysabccosworld_tasks_filesPinto2synth_data_qwen30Bmoe_binnedsplitsessaysshuffledessays.jsonlmsys1millpredictedlmsyspseudolabeldatalmsys_filtered_embeddingfinal_gen_data_lmsysSanyam_Hindisparsh-x-datasetfraud-detection-dataessaysjsonPintorider-detection-yoloonlymessagesinsta