Instructions to use keras/resnet_18_imagenet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasHub
How to use keras/resnet_18_imagenet with KerasHub:
import keras_hub import keras # Load ImageClassifier model image_classifier = keras_hub.models.ImageClassifier.from_preset( "hf://keras/resnet_18_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/resnet_18_imagenet") - Keras
How to use keras/resnet_18_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/resnet_18_imagenet") - Notebooks
- Google Colab
- Kaggle
| { | |
| "module": "keras_hub.src.models.resnet.resnet_image_classifier", | |
| "class_name": "ResNetImageClassifier", | |
| "config": { | |
| "backbone": { | |
| "module": "keras_hub.src.models.resnet.resnet_backbone", | |
| "class_name": "ResNetBackbone", | |
| "config": { | |
| "name": "res_net_backbone", | |
| "trainable": true, | |
| "input_conv_filters": [ | |
| 64 | |
| ], | |
| "input_conv_kernel_sizes": [ | |
| 7 | |
| ], | |
| "stackwise_num_filters": [ | |
| 64, | |
| 128, | |
| 256, | |
| 512 | |
| ], | |
| "stackwise_num_blocks": [ | |
| 2, | |
| 2, | |
| 2, | |
| 2 | |
| ], | |
| "stackwise_num_strides": [ | |
| 1, | |
| 2, | |
| 2, | |
| 2 | |
| ], | |
| "block_type": "basic_block", | |
| "use_pre_activation": false, | |
| "image_shape": [ | |
| null, | |
| null, | |
| 3 | |
| ] | |
| }, | |
| "registered_name": "keras_hub>ResNetBackbone" | |
| }, | |
| "preprocessor": { | |
| "module": "keras_hub.src.models.resnet.resnet_image_classifier_preprocessor", | |
| "class_name": "ResNetImageClassifierPreprocessor", | |
| "config": { | |
| "name": "res_net_image_classifier_preprocessor", | |
| "trainable": true, | |
| "dtype": { | |
| "module": "keras", | |
| "class_name": "DTypePolicy", | |
| "config": { | |
| "name": "float32" | |
| }, | |
| "registered_name": null | |
| }, | |
| "image_converter": { | |
| "module": "keras_hub.src.models.resnet.resnet_image_converter", | |
| "class_name": "ResNetImageConverter", | |
| "config": { | |
| "name": "res_net_image_converter", | |
| "trainable": true, | |
| "dtype": { | |
| "module": "keras", | |
| "class_name": "DTypePolicy", | |
| "config": { | |
| "name": "float32" | |
| }, | |
| "registered_name": null | |
| }, | |
| "image_size": [ | |
| 224, | |
| 224 | |
| ], | |
| "scale": [ | |
| 0.017124753831663668, | |
| 0.01750700280112045, | |
| 0.017429193899782133 | |
| ], | |
| "offset": [ | |
| -2.1179039301310043, | |
| -2.0357142857142856, | |
| -1.8044444444444445 | |
| ], | |
| "interpolation": "bicubic", | |
| "crop_to_aspect_ratio": true | |
| }, | |
| "registered_name": "keras_hub>ResNetImageConverter" | |
| }, | |
| "config_file": "preprocessor.json" | |
| }, | |
| "registered_name": "keras_hub>ResNetImageClassifierPreprocessor" | |
| }, | |
| "name": "res_net_image_classifier", | |
| "num_classes": 1000, | |
| "pooling": "avg", | |
| "activation": null, | |
| "dropout": 0.0 | |
| }, | |
| "registered_name": "keras_hub>ResNetImageClassifier" | |
| } |