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@@ -34,6 +34,26 @@ dataset_info:
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  download_size: 90167475
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  dataset_size: 90408406.0
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  ---
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- # Dataset Card for "road-detection"
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- [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  download_size: 90167475
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  dataset_size: 90408406.0
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  ---
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+ # About
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+ 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.
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+ For more info on the project we worked see this git [repo](https://github.com/balnarendrasapa/road-detection)
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+ # Dataset
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+ 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:
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+ - Images: This folder contains the original images, serving as the raw input data for the task at hand.
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+ - Segments: Here, you can access the labels specifically designed for Drivable Area Segmentation, crucial for understanding road structure and drivable areas.
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+ - Lane: This folder contains labels dedicated to Lane Detection, assisting in identifying and marking lanes on the road.
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+ # Downloading the dataset
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+ ```python
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+ from datasets import load_dataset
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+ dataset = load_dataset("bnsapa/road-detection")
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+ ```