| --- |
| license: mit |
| task_categories: |
| - video-classification |
| tags: |
| - RGB |
| - OPTICFLOW |
| - DASHCAM |
| - ROAD |
| - BEND |
| pretty_name: RGB and Optic Flow Bend Classification Dataset |
| --- |
| |
| [Data Viewer - View Samples](https://huggingface.co/datasets/aap9002/RGB_Optic_Flow_Bend_Classification/sql-console/djV2nZu) |
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| [Data Viewer - View Sample Count](https://huggingface.co/datasets/aap9002/RGB_Optic_Flow_Bend_Classification/sql-console/Jkn-dka) |
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| In our project on classifying the sharpness of bends using time-sequence data, we generated two distinct datasets: |
|
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| - **RGB Images:** Capturing conventional color information. |
| - **Wide View Dense Optic Flow:** Providing detailed motion dynamics. |
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| Notably, our latest dataset required approximately 16 hours to generate the samples using the code available in this [GitHub notebook](https://github.com/AAP9002/Third-Year-Project/blob/main/Solutions/extract_gps_data/gps_bend_finding_with_gaussian_clustering_with_catrisian_positions_and_circle_fitting_with_distance_based_angles.ipynb). |
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| To enhance organization and accessibility, we have migrated the trained models to a dedicated repository. You can explore the models and find additional documentation on [Hugging Face](https://huggingface.co/aap9002/RGB_Optic_Flow_Bend_Classification/). |
