Datasets:
license: apache-2.0
language:
- en
tags:
- driving
- autonomous-driving
- driving-dataset
- self-driving
- rural
- synthetic-data
- simulation
- computer-vision
- image-to-image
- image translation
- domain-adaptation
- weather
- time-of-day
- season
- driving-simulator
- autonomous-vehicles
- road-scenes
- outdoor
- sim-to-real
- domain-generalization
- data-augmentation
- perception
- scene-understanding
- visual-perception
- multi-domain
- environmental-conditions
task_categories:
- image-to-image
- image-segmentation
- robotics
task_ids:
- semantic-segmentation
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 75626482624
num_examples: 32000
- name: test
num_bytes: 6858464768
num_examples: 3200
download_size: 73104160837
dataset_size: 82484947392
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
🛣️MIST
Multi-Domain Synthetic Dataset for Rural Driving🌾
📘Dataset Introduction
MIST is a large-scale multi-domain synthetic dataset designed for rural driving scenarios. It provides explicitly structured domain factors—season, time of day, and weather—forming 32 balanced domain configurations.
🛠️Dataset Generation
MIST was generated using Slowroads (https://slowroads.io), a procedural rural road driving simulator. Images are rendered from a bumper-view camera at 1365 × 911 resolution.
📌 Dataset Status & Upload Plan
This repository is being uploaded incrementally.
✅Current upload
Image–text pair dataset (train/test) for image-to-image (I2I) translation
🔄In progress
Corresponding segmentation masks are currently being uploaded
📅Upcoming
- The full dataset (all splits + remaining annotations) will be released in subsequent updates
Note: The dataset is not yet complete. This README will be updated as additional components become available.