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
| 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. | |