datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
aidovecl-vehicle-detection-classification-localization
AIDOVECL: AI-generated Dataset of Outpainted Vehicles for Eye-level Classification and Localization
We introduce an annotated AI-generated dataset of eye-level vehicle images using outpainting, offering versatile generation of diverse vehicle classes in varied contexts with pretrained models.
Citation Notice
Please ensure that all publications and presentations using this data reference the following paper:
Kazemi, A., Fatima, Q. ul A., Kindratenko, V., & Tessum, C. W.… See the full description on the dataset page: https://huggingface.co/datasets/amir-kazemi/aidovecl-vehicle-detection-classification-localization.lca-bug-localization
🏟️ Long Code Arena (Bug localization)
This is the benchmark for the Bug localization task as part of the
🏟️ Long Code Arena benchmark.
The bug localization problem can be formulated as follows: given an issue with a bug description and a repository snapshot in a state where the bug is reproducible, identify the files within the repository that need to be modified to address the reported bug.
The dataset provides all the required components for evaluation of bug localization… See the full description on the dataset page: https://huggingface.co/datasets/JetBrains-Research/lca-bug-localization.AerialExtreMatch-Localization
AerialExtreMatch — Localization Dataset
Code | Project Page | Paper
This repo contains the localization set for our paper AerialExtreMatch: A Benchmark for Extreme-View Image Matching and Localization. Two different quality map and 264 query images are included. We also provide benchmark and train datasets.
Usage
Simply clone this repository.
git clone git@hf.co:datasets/Xecades/AerialExtreMatch-Train
Dataset Structure
.
├── HQref: *.tif
├── LQref: *.tif,*.prj… See the full description on the dataset page: https://huggingface.co/datasets/Xecades/AerialExtreMatch-Localization.lidar-localizationillusionVQA-Soft-Localization
IllusionVQA: Optical Illusion Dataset
Project Page |
Paper |
Github
TL;DR
IllusionVQA is a dataset of optical illusions and hard-to-interpret scenes designed to test the capability of Vision Language Models in comprehension and soft localization tasks. GPT4V achieved 62.99% accuracy on comprehension and 49.7% on localization, while humans achieved 91.03% and 100% respectively.
Usage
from datasets import load_dataset
import base64
from openai import OpenAI… See the full description on the dataset page: https://huggingface.co/datasets/csebuetnlp/illusionVQA-Soft-Localization.Image_Forgery_Localization_Datasetsin-store-visual-localizationDataset for monocular visual relocalization on a COLMAP 3D reconstruction model.
This dataset was collected at EZOHUB Tokyo with the cooperation of SATUDORA HOLDINGS CO.,LTD.
Dataset structure
train : Images used to construct the COLMAP model.
model : The COLMAP model files.
test : Test images to run relocalization.
intrinsic.xml : Intrinsic camera parameters.
full_pose_semantic_localization_dataset_gazeboChirpLoc100K___A_Synthetic_Spectrogram_Dataset_for_Chirp_Localization
Dataset Card for Chirp Spectrograms
Images & Corresponding Labels for Signal Processing Research
📊 Sample Generated Spectrograms
📝 Dataset Description
This dataset contains 100,000 synthetic chirp spectrograms with corresponding labels, generated for research in signal processing and machine learning applications. The dataset is particularly useful for developing and testing models for chirp signal detection and localization.
🧑💻… See the full description on the dataset page: https://huggingface.co/datasets/nubahador/ChirpLoc100K___A_Synthetic_Spectrogram_Dataset_for_Chirp_Localization.unknown_objects_semantic_localization_dataset_gazebo
The scene contains a majority of objects on which the model wasn't trained.
Viet-Localization-VQA
Dataset Overview
This dataset is was created from 56,989 Vietnamese 🇻🇳 localization images. The dataset includes quintessentially Vietnamese images such as scenic landscapes, historical sites, culinary specialties, festivals, cultural aspects from various regions, familiar rural scenes, and everyday life in urban areas, among others. Each image has been analyzed and annotated using advanced Visual Question Answering (VQA) techniques to produce a comprehensive dataset.
There is a… See the full description on the dataset page: https://huggingface.co/datasets/5CD-AI/Viet-Localization-VQA.partial_map_semantic_localization_dataset_gazebo
Map image contains the objects on which the model has been trained, but the scene has other objects too.
dataset_info:
features:
- name: case_id
dtype: string
- name: scene_id
dtype: int32
- name: camera
dtype: image
- name: lidar
dtype: image
- name: map
dtype: image
- name: pose
dtype: string
- name: grid
dtype: string
- name: orientation
dtype: string
- name: valid_cell
dtype: bool
- name: x_raw
dtype:… See the full description on the dataset page: https://huggingface.co/datasets/closedaxis-12573/partial_map_semantic_localization_dataset_gazebo.hardware_semantic_localization_datasetno_visible_object_localization_dataset_gazebollm_localization