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
| { | |
| "version": 1, | |
| "classes": [ | |
| "glass", | |
| "metal", | |
| "organic", | |
| "paper", | |
| "plastic" | |
| ], | |
| "image_size": 224, | |
| "model_type": "baseline_cnn", | |
| "batch_size": 32, | |
| "seed": 123, | |
| "epochs_ran": 24, | |
| "best_val_accuracy": 0.6768447756767273, | |
| "best_val_loss": 0.8843007683753967, | |
| "test_loss": 0.8580112457275391, | |
| "test_accuracy": 0.6952140927314758, | |
| "test_precision_macro": 0.6960417768138908, | |
| "test_recall_macro": 0.6916533741850668, | |
| "test_f1_macro": 0.6924287856071965, | |
| "preprocessing": "PIL RGB resize to 224x224; model contains Rescaling(1/255)", | |
| "augmentation": [ | |
| "RandomFlip(horizontal)", | |
| "RandomRotation(0.1)", | |
| "RandomZoom(0.1)", | |
| "RandomContrast(0.1)" | |
| ] | |
| } |