Instructions to use aap9002/RGB_Optic_Flow_Bend_Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use aap9002/RGB_Optic_Flow_Bend_Classification with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://aap9002/RGB_Optic_Flow_Bend_Classification") - Notebooks
- Google Colab
- Kaggle
| license: mit | |
| datasets: | |
| - aap9002/RGB_Optic_Flow_Bend_Classification | |
| pipeline_tag: video-classification | |
| library_name: keras | |
| Bend Classification Models | |
| This repository organises our trained models for classifying bend sharpness using time-sequence data from two datasets: RGB and Wide View Dense Optic Flow. | |
| Overview | |
| Model Varients: | |
| - RGB Images | |
| - Wide View Dense Optic Flow | |
| Our dataset: | |
| [https://huggingface.co/datasets/aap9002/RGB_Optic_Flow_Bend_Classification](https://huggingface.co/datasets/aap9002/RGB_Optic_Flow_Bend_Classification) | |
| Our Training Script: | |
| [https://github.com/AAP9002/Third-Year-Project/blob/main/nn/left_right_classification.ipynb](https://github.com/AAP9002/Third-Year-Project/blob/main/nn/left_right_classification.ipynb) |