Instructions to use kolkela/simple-cnn-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use kolkela/simple-cnn-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://kolkela/simple-cnn-classifier") - Notebooks
- Google Colab
- Kaggle
| language: en | |
| tags: | |
| - keras | |
| - computer-vision | |
| - image-classification | |
| - cnn | |
| license: mit | |
| # Simple CNN Classifier | |
| ## Model Architecture | |
| - **Data Augmentation:** Random Flip, Rotation, Zoom, Contrast | |
| - **Conv Blocks:** 32 -> 64 -> 128 filters + MaxPool | |
| - **Regularization:** Dropout | |
| - **Head:** GlobalAveragePooling2D -> Dense Layer | |
| ## How to Load & Use | |
| ```python | |
| import keras | |
| model = keras.saving.load_model("hf://kolkela/simple-cnn-classifier") |