Instructions to use keras/efficientnet2_rw_t_ra2_imagenet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasHub
How to use keras/efficientnet2_rw_t_ra2_imagenet with KerasHub:
import keras_hub import keras # Load ImageClassifier model image_classifier = keras_hub.models.ImageClassifier.from_preset( "hf://keras/efficientnet2_rw_t_ra2_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/efficientnet2_rw_t_ra2_imagenet") - Keras
How to use keras/efficientnet2_rw_t_ra2_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/efficientnet2_rw_t_ra2_imagenet") - Notebooks
- Google Colab
- Kaggle
File size: 5,863 Bytes
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"module": "keras_hub.src.models.efficientnet.efficientnet_image_classifier",
"class_name": "EfficientNetImageClassifier",
"config": {
"backbone": {
"module": "keras_hub.src.models.efficientnet.efficientnet_backbone",
"class_name": "EfficientNetBackbone",
"config": {
"name": "efficient_net_backbone",
"trainable": true,
"stackwise_width_coefficients": [
0.8,
0.8,
0.8,
0.8,
0.8,
0.8
],
"stackwise_depth_coefficients": [
0.9,
0.9,
0.9,
0.9,
0.9,
0.9
],
"dropout": 0,
"depth_divisor": 8,
"min_depth": null,
"activation": "silu",
"input_shape": [
null,
null,
3
],
"stackwise_kernel_sizes": [
3,
3,
3,
3,
3,
3
],
"stackwise_num_repeats": [
2,
4,
4,
6,
9,
15
],
"stackwise_input_filters": [
24,
24,
48,
64,
128,
160
],
"stackwise_output_filters": [
24,
48,
64,
128,
160,
256
],
"stackwise_expansion_ratios": [
1,
4,
4,
4,
6,
6
],
"stackwise_squeeze_and_excite_ratios": [
0,
0,
0,
0.25,
0.25,
0.25
],
"stackwise_strides": [
1,
2,
2,
2,
1,
2
],
"stackwise_block_types": [
"cba",
"fused",
"fused",
"unfused",
"unfused",
"unfused"
],
"stackwise_force_input_filters": [
0,
0,
0,
0,
0,
0
],
"include_stem_padding": true,
"use_depth_divisor_as_min_depth": true,
"cap_round_filter_decrease": true,
"stem_conv_padding": "valid",
"batch_norm_momentum": 0.9,
"batch_norm_epsilon": 1e-05,
"projection_activation": null
},
"registered_name": "keras_hub>EfficientNetBackbone"
},
"preprocessor": {
"module": "keras_hub.src.models.efficientnet.efficientnet_image_classifier_preprocessor",
"class_name": "EfficientNetImageClassifierPreprocessor",
"config": {
"name": "efficient_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.efficientnet.efficientnet_image_converter",
"class_name": "EfficientNetImageConverter",
"config": {
"name": "efficient_net_image_converter",
"trainable": true,
"dtype": {
"module": "keras",
"class_name": "DTypePolicy",
"config": {
"name": "float32"
},
"registered_name": null
},
"image_size": [
224,
224
],
"scale": [
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0.017429193899782133
],
"offset": [
-2.1179039301310043,
-2.0357142857142856,
-1.8044444444444445
],
"interpolation": "bicubic",
"crop_to_aspect_ratio": true
},
"registered_name": "keras_hub>EfficientNetImageConverter"
},
"config_file": "preprocessor.json"
},
"registered_name": "keras_hub>EfficientNetImageClassifierPreprocessor"
},
"name": "efficient_net_image_classifier",
"num_classes": 1000,
"pooling": "avg",
"activation": null,
"dropout": 0.0
},
"registered_name": "keras_hub>EfficientNetImageClassifier"
} |