Instructions to use chabdullah0566/Omnivision_Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use chabdullah0566/Omnivision_Classifier with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://chabdullah0566/Omnivision_Classifier") - Notebooks
- Google Colab
- Kaggle
File size: 663 Bytes
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---
license: mit
tags:
- keras
- tensorflow
- computer-vision
- image-classification
- convnext
- cifar100
library_name: keras
---
# Omnivision_Classifier
## Model Description
An advanced Computer Vision model trained to classify **30 highly overlapping classes** from the CIFAR-100 dataset using **ConvNeXtTiny**.
## Files in this Repository
- `best_model.keras`: The trained Keras model weights.
- `class_names.json`: List of the 30 selected classes.
- `model_comparison.csv`: A CSV file comparing the performance metrics of different models.
## Trained Classes (30)
Fruits & Vegetables, Large Carnivores, Large Omnivores, Small Mammals, Vehicles, People.
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