Instructions to use Tushansh/ayush-medicinal-plant-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use Tushansh/ayush-medicinal-plant-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://Tushansh/ayush-medicinal-plant-classifier") - Notebooks
- Google Colab
- Kaggle
| { | |
| "model_type": "image-classification", | |
| "task": "image-classification", | |
| "framework": "keras", | |
| "architectures": [ | |
| "Xception" | |
| ], | |
| "num_classes": 7, | |
| "image_size": 299, | |
| "labels": [ | |
| "Aloevera", | |
| "Amla", | |
| "Bhrami", | |
| "Ginger", | |
| "Neem", | |
| "Tulsi", | |
| "Turmeric" | |
| ], | |
| "id2label": { | |
| "0": "Aloevera", | |
| "1": "Amla", | |
| "2": "Bhrami", | |
| "3": "Ginger", | |
| "4": "Neem", | |
| "5": "Tulsi", | |
| "6": "Turmeric" | |
| }, | |
| "label2id": { | |
| "Aloevera": 0, | |
| "Amla": 1, | |
| "Bhrami": 2, | |
| "Ginger": 3, | |
| "Neem": 4, | |
| "Tulsi": 5, | |
| "Turmeric": 6 | |
| }, | |
| "preprocessing": { | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "size": 299, | |
| "do_resize": true, | |
| "do_normalize": true | |
| } | |
| } |