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
File size: 738 Bytes
ab0aeff | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | {
"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)"
]
} |