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
File size: 759 Bytes
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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) |