Instructions to use nico9ga/fruits-Classification-Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use nico9ga/fruits-Classification-Model with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://nico9ga/fruits-Classification-Model") - Notebooks
- Google Colab
- Kaggle
| tags: | |
| - image-classification | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: fruits | |
| results: | |
| - task: | |
| name: Image Classification | |
| type: image-classification | |
| metrics: | |
| - name: Accuracy | |
| type: accuracy | |
| value: 0.9896 | |
| license: unlicense | |
| language: | |
| - es | |
| library_name: keras | |
| <h1>Clasificador de Frutas</h1> | |
| <h3>Este modelo es para la clasificacion de ciertos tipos de frutas dentro de las cuales encontramos: </h3> | |
| <h4>-Banano</h4> | |
| <h4>-Durazno</h4> | |
| <h4>-Fresa</h4> | |
| <h4>-Guayaba</h4> | |
| <h4>-Kiwi</h4> | |
| <h4>-Limon</h4> | |
| <h4>-Mandarina</h4> | |
| <h4>-Mango</h4> | |
| <h4>-Manzana</h4> | |
| <h4>-Manzana Verde</h4> | |
| <h4>-Melon</h4> | |
| <h4>-Naranja</h4> | |
| <h4>-Papaya</h4> | |
| <h4>-Pera</h4> | |
| <h4>-Piña</h4> | |
| <h4>-Sandia</h4> | |
| <h4>-Tomate de Arbol</h4> | |