Instructions to use keras/swin_tiny_patch4_window7_224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasHub
How to use keras/swin_tiny_patch4_window7_224 with KerasHub:
import keras_hub import keras # Load ImageClassifier model image_classifier = keras_hub.models.ImageClassifier.from_preset( "hf://keras/swin_tiny_patch4_window7_224", 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/swin_tiny_patch4_window7_224") - Keras
How to use keras/swin_tiny_patch4_window7_224 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/swin_tiny_patch4_window7_224") - Notebooks
- Google Colab
- Kaggle
| { | |
| "module": "keras_hub.src.models.swin_transformer.swin_transformer_backbone", | |
| "class_name": "SwinTransformerBackbone", | |
| "config": { | |
| "name": "swin_transformer_backbone", | |
| "trainable": true, | |
| "dtype": { | |
| "module": "keras", | |
| "class_name": "DTypePolicy", | |
| "config": { | |
| "name": "float32" | |
| }, | |
| "registered_name": null | |
| }, | |
| "image_shape": [ | |
| 224, | |
| 224, | |
| 3 | |
| ], | |
| "patch_size": 4, | |
| "embed_dim": 96, | |
| "depths": [ | |
| 2, | |
| 2, | |
| 6, | |
| 2 | |
| ], | |
| "num_heads": [ | |
| 3, | |
| 6, | |
| 12, | |
| 24 | |
| ], | |
| "window_size": 7, | |
| "mlp_ratio": 4.0, | |
| "qkv_bias": true, | |
| "dropout_rate": 0.0, | |
| "attention_dropout": 0.0, | |
| "drop_path": 0.1, | |
| "patch_norm": true, | |
| "data_format": "channels_last" | |
| }, | |
| "registered_name": "keras_hub>SwinTransformerBackbone" | |
| } |