---
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)

[Data Viewer - View Sample Count](https://huggingface.co/datasets/aap9002/RGB_Optic_Flow_Bend_Classification/sql-console/Jkn-dka)

In our project on classifying the sharpness of bends using time-sequence data, we generated two distinct datasets:

- **RGB Images:** Capturing conventional color information.
- **Wide View Dense Optic Flow:** Providing detailed motion dynamics.

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).

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/).