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--- |
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license: gpl-3.0 |
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size_categories: |
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- n<1K |
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task_categories: |
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- image-to-image |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: test |
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path: data/test-* |
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- split: validation |
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path: data/validation-* |
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dataset_info: |
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features: |
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- name: image |
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dtype: image |
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- name: segment |
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dtype: image |
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- name: lane |
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dtype: image |
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splits: |
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- name: train |
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num_bytes: 72551321.0 |
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num_examples: 160 |
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- name: test |
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num_bytes: 8756556.0 |
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num_examples: 20 |
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- name: validation |
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num_bytes: 9100529.0 |
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num_examples: 20 |
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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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``` |
