Instructions to use keras/deit_tiny_distilled_patch16_224_imagenet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasHub
How to use keras/deit_tiny_distilled_patch16_224_imagenet with KerasHub:
import keras_hub import keras # Load ImageClassifier model image_classifier = keras_hub.models.ImageClassifier.from_preset( "hf://keras/deit_tiny_distilled_patch16_224_imagenet", num_classes=2, ) # Fine-tune image_classifier.fit( x=keras.random.randint((32, 64, 64, 3), 0, 256), y=keras.random.randint((32, 1), 0, 2), ) # Classify image image_classifier.predict(keras.random.randint((1, 64, 64, 3), 0, 256))import keras_hub # Create a Backbone model unspecialized for any task backbone = keras_hub.models.Backbone.from_preset("hf://keras/deit_tiny_distilled_patch16_224_imagenet") - Keras
How to use keras/deit_tiny_distilled_patch16_224_imagenet with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://keras/deit_tiny_distilled_patch16_224_imagenet") - Notebooks
- Google Colab
- Kaggle
| { | |
| "module": "keras_hub.src.models.deit.deit_backbone", | |
| "class_name": "DeiTBackbone", | |
| "config": { | |
| "name": "dei_t_backbone", | |
| "trainable": true, | |
| "image_shape": [ | |
| 224, | |
| 224, | |
| 3 | |
| ], | |
| "patch_size": [ | |
| 16, | |
| 16 | |
| ], | |
| "num_layers": 12, | |
| "num_heads": 3, | |
| "hidden_dim": 192, | |
| "intermediate_dim": 768, | |
| "dropout_rate": 0.0, | |
| "attention_dropout": 0.0, | |
| "layer_norm_epsilon": 1e-12, | |
| "use_mha_bias": true | |
| }, | |
| "registered_name": "keras_hub>DeiTBackbone" | |
| } |