Instructions to use Raditsoic/trashnet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use Raditsoic/trashnet with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://Raditsoic/trashnet") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architecture": "Tensorflow CNN", | |
| "input_size": [ | |
| 224, | |
| 224, | |
| 3 | |
| ], | |
| "num_classes": 6, | |
| "augmentation": { | |
| "rotation_range": 20, | |
| "width_shift_range": 0.2, | |
| "height_shift_range": 0.2, | |
| "horizontal_flip": true, | |
| "vertical_flip": true, | |
| "shear_range": 0.2, | |
| "brightness_range": [ | |
| 0.8, | |
| 1.2 | |
| ] | |
| }, | |
| "optimizer": "adam", | |
| "learning_rate": 0.001, | |
| "batch_size": 32, | |
| "epochs": 50 | |
| } |