Datasets:
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---
license: gpl-3.0
size_categories:
- n<1K
task_categories:
- image-to-image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: image
dtype: image
- name: segment
dtype: image
- name: lane
dtype: image
splits:
- name: train
num_bytes: 72551321.0
num_examples: 160
- name: test
num_bytes: 8756556.0
num_examples: 20
- name: validation
num_bytes: 9100529.0
num_examples: 20
download_size: 90167475
dataset_size: 90408406.0
---
# About
This dataset is for detecting the drivable area and lane lines on the roads. Images are generated using stable diffusion model and images are annotated using labelme annotator.
For more info on the project we worked see this git [repo](https://github.com/balnarendrasapa/road-detection)
# Dataset
The dataset is structured into three distinct partitions: Train, Test, and Validation. The Train split comprises 80% of the dataset, containing both the input images and their corresponding labels. Meanwhile, the Test and Validation splits each contain 10% of the data, with a similar structure, consisting of image data and label information. Within each of these splits, there are three folders:
- Images: This folder contains the original images, serving as the raw input data for the task at hand.
- Segments: Here, you can access the labels specifically designed for Drivable Area Segmentation, crucial for understanding road structure and drivable areas.
- Lane: This folder contains labels dedicated to Lane Detection, assisting in identifying and marking lanes on the road.
# Downloading the dataset
```python
from datasets import load_dataset
dataset = load_dataset("bnsapa/road-detection")
``` |